Vitiligo is a chronic immune-mediated depigmenting disorder that is strongly associated with autoimmune thyroid disease, the most common autoimmune comorbidity reported in affected patients. Although thyroid abnormalities occur more frequently in patients with vitiligo than in the general population, recommendations regarding routine thyroid screening remain variable. Increasing evidence suggests that thyroid disease risk is not uniform across all patients with vitiligo and may be influenced by specific clinical and phenotypic characteristics. This narrative review examines the epidemiology, temporal relationship, clinical predictors, and proposed pathogenic mechanisms linking vitiligo and thyroid disease, with a focus on practical screening considerations. Current evidence suggests that several clinical and phenotypic features are associated with an increased risk of thyroid dysfunction and autoimmune thyroid disease in patients with vitiligo, including nonsegmental vitiligo, acral involvement, female sex, extensive or progressive disease, longer disease duration, family history of thyroid disease or autoimmunity, the presence of additional autoimmune disorders, and symptoms suggestive of thyroid dysfunction. Thyroid autoantibody positivity is among the most frequently reported markers of thyroid autoimmunity, while subclinical hypothyroidism is among the most common forms of thyroid dysfunction in patients with vitiligo. Thyroid disease may precede or follow the onset of vitiligo, highlighting the importance of ongoing clinical awareness and individualized risk assessment. A targeted, phenotype-guided approach to screening may improve diagnostic yield while reducing unnecessary testing in lower-risk individuals. Thyroid-stimulating hormone remains the most practical initial screening test, with additional laboratory evaluation guided by clinical presentation and individual risk factors. By integrating clinical phenotype, medical history, family history, symptoms, and physical examination findings into screening decisions, clinicians may be better positioned to identify patients most likely to benefit from thyroid evaluation and longitudinal follow-up while avoiding unnecessary testing. This practical approach may be particularly useful in primary care and dermatology settings.
The implementation of digital pathology (DP) has been reported across academic, public, and private laboratory settings, providing valuable insights into workflow transformation. However, comparatively little guidance exists on how DP systems should be evaluated prior to acquisition and deployment. Given the substantial financial investment and long-term strategic implications of DP infrastructure, a structured and context-aware evaluation process is essential. In this practical guide, developed by the European Society of Digital and Integrative Pathology (ESDIP) https://www.esdipath.org/, we outline a comprehensive approach for the pre-deployment assessment of DP systems. Drawing on collective experience from multiple institutions and supported by relevant literature, we describe key parameters to consider when evaluating the core components of the DP ecosystem: whole slide scanners, image management systems, workstations, storage infrastructure, and laboratory information systems. We highlight methodological differences between large-scale multisite procurement processes and single-institution implementations, emphasising realistic workload testing, interoperability, and long-term scalability. By integrating technical and clinical perspectives, this guide aims to support pathology departments, IT teams, and institutional decision-makers in selecting DP solutions aligned with their specific institutional needs, infrastructure capabilities, and the future integration of computational pathology. © 2026 The Author(s). The Journal of Pathology published by John Wiley & Sons Ltd on behalf of The Pathological Society of Great Britain and Ireland.
Pharmacists are expanding their participation in veterinary healthcare teams and assuming roles beyond traditional dispensing duties. However, the scope of these collaborative practices and the degree of mutual recognition vary substantially across regions. This scoping review aimed to organize the existing literature on collaboration between veterinarians and pharmacists, clarifying current roles, practical applications, and professional perceptions within the veterinary field. A scoping review was conducted to examine the roles, practices, and perceptions associated with veterinarian-pharmacist collaboration in veterinary medicine and related fields. Two researchers independently searched PubMed, Web of Science, the Cochrane Library, and Ichushi-Web, and additionally screened Google Scholar to identify gray literature (from database inception to August 2025, in English or Japanese). Records were independently screened at the title/abstract and full-text levels using predefined eligibility criteria, and relevant studies were identified. The search yielded 239 records, of which 16 studies published between 2007 and 2024 were included. Studies were conducted primarily in the United States (n = 7), New Zealand (n = 3), and Japan (n = 3); one study collected data from both Japan and Taiwan. Most studies employed cross-sectional survey designs. Pharmacist roles most frequently involved compounding and dispensing for animal patients (62.5%), followed by drug information (DI) and consultation (37.5%), inventory and supply management (25.0%), client education (18.8%), and safety and exposure control (12.5%). This scoping review demonstrates that veterinarian-pharmacist collaboration is described within a limited and regionally variable evidence base, with pharmacists most often contributing through compounding/dispensing and drug information support. Sustained and scalable implementation will require improved mutual understanding of professional roles, strengthened veterinary-specific education for pharmacists, and more robust empirical research to inform collaborative practice models.
Browning is a major postharvest and processing-related quality problem in yam (Dioscorea spp.), but its occurrence and control depend strongly on product form. This review summarizes yam browning from a product-specific, mechanism-based, and application-oriented perspective, covering whole yam tubers, fresh-cut slices, yam purée/paste, and thermally processed products. Whole tubers are mainly affected by storage conditions, wound responses, and oxygen exposure, while fresh-cut slices rapidly develop polyphenol oxidase (PPO)/peroxidase (POD)-mediated enzymatic browning after tissue disruption. Yam purée and paste may involve enzymatic oxidation, oxygen diffusion, and pigment-related color changes, whereas dried or heated products are more associated with Maillard reaction, thermal darkening, and moisture-dependent non-enzymatic browning. Bisdemethoxycurcumin (BDMC)-related yellowing is also highlighted as a distinctive color-change pathway in yam. Based on these mechanisms, key control targets include oxygen restriction, enzyme activity reduction, quinone reduction, BDMC/yellowing regulation, and moisture-thermal management. Representative strategies, including organic acid or antioxidant dips, edible coatings with modified atmosphere packaging, optimized storage, drying/heating control, spectroscopic monitoring, and intelligent cold-chain management, are critically compared in terms of advantages, limitations, cost-effectiveness, scalability, and effects on nutritional and functional quality.
This study aims to explore a new model of tiered medication therapy management (MTM) services for outpatients with bronchial asthma and evaluate its effectiveness. A new model of tiered MTM services for outpatients with asthma (referred to as the T-MTM model) was established based on the triangle risk stratification model, and its multidimensional value was evaluated using the ECHO model. A single-center, prospective, randomized controlled, open-label study was conducted. A total of 126 asthma patients who visited the Medication Therapy Clinic at Hebei Provincial People's Hospital from January 2024 to December 2024 were selected and randomly divided into an observation group and a control group, with 63 patients in each group. Patients in the observation group received T-MTM services, while those in the control group received traditional MTM services. The differences in values across the three dimensions-economic, clinical, and humanistic-were compared between the two groups before and after the intervention. Before the intervention, there were no statistically significant differences in any indicators between the two groups (p > 0.05). After the intervention, the patients in the observation group who received T-MTM services demonstrated superior outcomes in economic, clinical, and humanistic values than in those in the control group, with statistically significant differences (p < 0.05). The mean difference in the cost-utility ratio is as follows: MD = -7.54 (95% CI, -9.38 to -5.70), whereas that in the ACT score is given as MD = 2.25 (95% CI, 1.41-3.09). The improvement in the observation group was 5.05 points, exceeding the minimum clinically important difference (MCID) threshold of 3 points (MARS-A adherence score: MD = 0.44 [95% CI, 0.25-0.63]). The average duration of a single service per pharmacist was reduced by 18.92 min, representing an efficiency improvement of approximately 35%. Risk-stratified tiered MTM services enable pharmacists to rapidly identify high-risk asthma patients, significantly improve work efficiency and service volume, and simultaneously enhance patients' self-management capabilities. This approach provides evidence-based guidance for optimizing and tiering outpatient asthma pharmacy service resources and represents a modified pharmacy service model worthy of promotion.
Artificial tick feeding systems (ATFS) provide a valuable alternative to animal-based models for studying tick biology. Ixodes hirsti, an Australian tick species that parasitises marsupials, remains understudied due to challenges in laboratory maintenance. Here, we report the first successful in vitro feeding of I. hirsti larvae, provide preliminary microbiome profiles of unfed larvae and larvae recovered after artificial feeding and present the first molecularly confirmed morphological description of the nymphal stage. Field-collected engorged females of I. hirsti were allowed to oviposit under laboratory conditions. Hatched larvae were artificially fed on blood using silicone membranes supplemented with kangaroo hair and/or kangaroo hair extract. Microbiomes were characterised by 16S rRNA amplicon sequencing, while scanning electron microscopy (SEM) and sequencing of 16S rRNA and cox1 genes were used for morphological and molecular characterisation of nymphs. Membranes treated with hair extract alone yielded the highest attachment rate (71%), whereas kangaroo hair-treated membranes produced superior feeding performance, with shorter time to engorgement (9.26 ± 1.00 days) and a higher engorgement weight (0.91 ± 0.01 mg). Exploratory microbiome profiling showed that fed larval pools had numerically lower microbial richness and evenness than unfed larval pools, although these differences were not significant. A total of 80 microbial taxa were shared between groups, whereas seven and 17 taxa were unique to fed and unfed larvae, respectively. Stenotrophomonas was more abundant in fed larval pools, while Coxiella-like and Rickettsia-like endosymbionts were detected in both fed and unfed larvae. These findings demonstrate that ATFS can be adapted for wildlife-associated ticks with specialised host preferences and provide a practical framework for investigating the biology and microbial ecology of ticks.
To evaluate the prognostic significance of nutritional-inflammatory biomarkers in non-small cell lung cancer (NSCLC) patients with bone metastases. The present study constructed prognostic models using machine learning methods and assessed their performance, aiming to develop a clinically practical nomogram. A retrospective analysis of 233 patients with NSCLC and confirmed bone metastasis (BM) was conducted. The present study analyzed clinical and laboratory data, including 10 nutritional-inflammatory indicators. The present study used univariate and multivariate Cox regression, Least Absolute Shrinkage and Selection Operator (LASSO), Random Forest and extreme gradient boosting to select variables and construct Cox models. Performance was assessed via C-index, time-dependent area under the curve, Brier score, calibration curve and Akaike information criterion (AIC). A nomogram was developed based on the best-performing model. Multivariate Cox regression identified history of primary tumor surgery [hazard ratio (HR)=0.35; P<0.001], hemoglobin (HR=0.99; P=0.02), prognostic nutritional index (HR=0.98; P=0.002), CYFRA21-1 (HR=1.01; P<0.001), neuron-specific enolase (NSE; HR=1.03; P<0.001) and total cholesterol (HR=1.05; P=0.006) as independent prognostic factors. Individual nutritional-inflammatory biomarkers demonstrated limited discrimination (C-index range: 0.48-0.58). By contrast, integrated models incorporating these markers markedly improved predictive performance. The LASSO_yes model achieved the highest C-index (0.74; 95% CI: 0.70-0.77), with a 24-month area under the curve of 0.79 and the lowest Akaike information criterion (AIC; 1684.6). Based on the best-performing model, a prognostic nomogram was constructed to estimate individualized survival probabilities. Nutritional-inflammatory biomarkers provide incremental prognostic value when incorporated into integrated models. The LASSO-based nomogram may provide a potentially practical tool for individualized survival prediction in patients with NSCLC with BM, although external validation is still required before broader clinical application.
ISO 15189:2022 requires medical laboratories to estimate measurement uncertainty (MU), and ISO/TS 20914:2019 provides the practical guidance for doing so, typically through a top-down approach in which the within-laboratory reproducibility standard uncertainty (uRw) is derived from 6 to 12 months of internal quality control (IQC) data. This criterion is defined by the routine sources of variation captured, not by the window over which results are actually compared. For acute, single-use tests the two coincide; for analytes used in long-term monitoring - thyroglobulin, PSA, creatinine/eGFR, and many tumor markers and endocrine parameters - a result from 2021 may be compared with one from 2026, and the relevant uncertainty spans every reagent and calibrator lot, recalibration and maintenance event within that interval, whatever the analyte. Where a valid estimate of within-subject biological variation is available, the index of individuality identifies the analytes for which this shortfall matters most, interpretation then resting on the reference change value. This letter proposes aligning the duration of IQC data with the clinical application window of the test. Partitioning by reagent and QC lot, as recommended by CLSI C24 and ISO/TS 20914, keeps long-term estimates sound. This perspective extends the "Measurement Uncertainty for Practical Use" (MUPU) model to the temporal axis, advocating a shift from idealised short-term precision to pragmatic, clinically fit-for-purpose MU. A two-stage scheme is recommended: a rolling annual uRw for analytical trends and an aggregate long-term uRw for clinical reporting. Future guideline revisions should individualise the uRw duration according to the clinical use window of the test.
Kawasaki disease (KD) and viral infection (VD) share similar clinical features but require distinct treatments. A practical biomarker to distinguish them is therefore clinically important. Previous blood transcriptome studies identified many differentially expressed genes, but large gene panels are impractical for routine laboratories. A ratio-based Direct Leukocyte Single-cell-type Transcript Abundance (DIRECT LS-TA) assay was recently developed to quantify monocyte gene expression directly in whole blood using a monocyte-specific target gene relative to monocyte reference genes (PSAP or CTSS). Interferon-stimulated genes IFI27, IFI44L, and SIGLEC1 can be measured by this approach. In this study, three ratio biomarkers (IFI27/PSAP, IFI44L/PSAP, SIGLEC1/PSAP) and a conventional interferon (IFN) score derived from eight genes were calculated from public blood transcriptome datasets (GSE73461 and GSE68004) and compared between KD and VD. VD patients showed markedly elevated IFN-related biomarkers, with all three ratios significantly higher in VD and IFI27/PSAP giving the largest increase. IFI27/PSAP achieved the highest diagnostic performance (AUC 0.90), slightly exceeding the conventional IFN score (AUC 0.89). These findings suggest that absent or minimal IFN activation argues against VD and supports KD, and that this simple ratio assay could serve as a clinically useful exclusion test.
The application of next-generation sequencing (NGS) is rapidly expanding for antimicrobial resistance (AMR) surveillance and clinical decision-making. However, despite a high AMR burden, many low- and middle-income countries lack the infrastructure and technical capacity required to implement sequencing-based approaches. To address this gap and demonstrate the practical application of the technology, we compared the performance of nanopore sequencing combined with real-time analysis using the EPI2ME platform in a mobile laboratory with that of a hybrid sequencing approach integrating Illumina short reads and nanopore long reads. In this exploratory diagnostic evaluation study, 25 Escherichia coli isolates obtained through the German annual national AMR monitoring program were included. Isolates resistant to at least one β-lactam antibiotic were considered as resistant isolate. Genomic DNA was subjected to both Illumina and nanopore sequencing. Following the sequencing, the EPI2ME whole genome sequencing pipeline was used to detect and identify β-lactamase genes in E. coli isolates. As the comparator, both long and short read-based hybrid assemblies were used to detect and identify the β-lactamase genes. The sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV) of the sequencing approaches were calculated against the phenotypic classification of E. coli isolates, treating the presence or absence of β-lactamase-encoding genes as the genotypic predictor of β-lactam resistance. A total of 36 β-lactamase-encoding genes, predominantly from the bla CTX-M, bla SHV, and bla TEM families, were detected. Both analytical approaches achieved a promising accuracy towards predicting β-lactam resistance. Moderate agreement was observed between methods, with an overall concordance of 68.9%. A distinct distribution pattern of β-lactamase genes was observed across the MIC ranges of the isolates for third-generation cephalosporins and the carbapenems. These findings support the feasibility of deploying nanopore sequencing in mobile suitcase laboratories to strengthen AMR surveillance in food production systems. Given its portability, rapid turnaround time, and minimal infrastructure requirements, the approach may also have broader applications for One Health based AMR surveillance, clinical decision-making, and outbreak investigations in resource-limited settings.
In renal transplant recipients, infections can have atypical outcomes due to immunosuppression therapy, and delayed diagnosis causes high mortality. We developed an infection risk score in renal transplant recipients to rapidly predict infection risk in emergency departments. Of 870 renal transplant recipients admitted to the emergency department, we included 608 patients and 262 control cases. Hospital record system data for renal transplant recipients were retrospectively investigated for the period January 2021 through December 2025. Laboratory and vital signs of patients suspected of infection were compared versus asymptomatic control cases admitted for routine check -ups. All of our patients metethical standards and were selected as related donors and recipients. Demographic characteristics and findings of 608 patients and 262 control cases were compared, and no significant difference was found between the 2 groups in terms of comorbidities (P > .05 ). The most frequent presenting symptom was diarrhea (20.1 % ); the least frequent symptom was sore throat (6.6 % ). Leukocytes, neutrophils, neutrophil -lymphocyte ratio, plasma -lymphocyte ratio, C -reactive protein, and sodium and potassium levels, as well as vital signs including fever, pulse, blood pressure, and peripheral oxygen saturation, were associated with infection. This novel infection risk score comprised 6 parameters, including C-reactive protein, neutrophil -lymphocyte ratio, sodium, fever, pulse, and systolic blood pressure, predicted infections with 89.1 % (area under the curve ) accuracy. Sensitivity, specificity, positive predictive value, and negative predictive value of the score were 0.734, 0.905, 0.947, and 0.594, respectively. The novel infection risk score developed in our study predicts infections in renal transplant recipients with high accuracy using only routine biochemical and vital parameters. This practical, rapid, and reliable system can contribute to early diagnosis processes in emergency departments. Future external validation through multicenter studies is needed to support its integration into clinical guidelines.
Itaconic acid (IA), an important unsaturated dicarboxylic acid, finds wide applications in industry, medicine, food, and energy. Biotechnological production of IA offers advantages in sustainability, process controllability, and the potential for high titers in selected hosts, although cost competitiveness remains a major barrier to industrial deployment. However, several challenges still hinder its large-scale industrial production, including: low substrate utilization efficiency, difficulty in pathway regulation, downstream separation bottlenecks, and environmental concerns. To address these challenges and further improve IA production through metabolic engineering, this review summarizes recent advances and key technologies in IA biosynthesis. Engineering strategies for de novo IA production were analyzed, the application of whole-cell catalysis and fermentation process optimization to enhance IA yield was discussed, and the use of renewable resources as substrates for IA production was reviewed. In addition, the prospects of AI-assisted strain engineering and green, low-carbon process technologies for IA biosynthesis were examined. These insights provide valuable guidance for understanding metabolic engineering strategies and bioprocess innovations aimed at improving IA production in alignment with sustainable and low-carbon objectives. Industrial demand for bio-based itaconic acid is rising, yet scale-up remains constrained by suboptimal pathway control, transport bottlenecks, and energy-intensive downstream steps. This review integrates advances across strain and process engineering into a coherent playbook: mitochondrial/cytosolic rerouting and transporter tuning, phase-specific dynamic regulation (biosensors, CRISPRi), and low-pH, closed-loop separations. We benchmark renewable feedstocks (methanol, acetate, agricultural and industrial wastes) and align data driven/AI tools with TEA/LCA targets. The result is a practical roadmap to higher titers, lower costs, and greener IA biomanufacturing from lab to pilot.
Extracorporeal membrane oxygenation (ECMO) in patients following intracerebral hemorrhage (ICH) poses a critical dilemma between circuit thrombosis and catastrophic rebleeding. While nafamostat mesylate (NM) has been increasingly explored for ECMO anticoagulation in patients with ICH, it is conventionally administered as a systemic anticoagulant with high activated partial thromboplastin time (aPTT) targets, which still carries significant bleeding risks. Moreover, whether NM can achieve effective regional anticoagulation in venovenous (VV) ECMO remains highly controversial. Here, we present a post-ICH patient with severe ARDS who required VV-ECMO. Due to suspected rebleeding, anticoagulation was transitioned from heparin to an anticoagulant-free strategy, which subsequently led to significant oxygenator thrombosis. To navigate this crisis, NM was initiated with an ultra-low systemic target, but it initially failed to establish a regional anticoagulant effect when infused at the conventional pre-membrane, post-pump site. Crucially, relocating the NM infusion to the pre-pump position successfully established a significant circuit-to-systemic aPTT gradient. This technical optimization enabled effective circuit anticoagulation while minimizing systemic bleeding risk, allowing for successful ECMO weaning without bleeding complications. This dynamic intra-patient observation highlights the potential impact of infusion site on coagulation outcomes. We hypothesize that harnessing the centrifugal pump's high-shear turbulence for homogenous drug mixing might serve as a crucial prerequisite for achieving regional anticoagulation in VV-ECMO. These findings bridge the gap between theoretical pharmacology and practical circuit engineering, offering a practical preliminary reference for the future anticoagulant management of similar high-bleeding-risk patients on ECMO.
The staining performance of hemalum solutions is critically dependent on pH, yet the acetic acid content varies among different hemalum formulations. This study aimed to clarify the relationship between the acetic acid ratio (pH) of Gill's hemalum and optimal nuclear staining, and to improve the hematoxylin and eosin (H&E) staining method. Gill's hemalum with varying acetic acid concentrations was used for H&E staining of surgical and biopsy specimens. Optimal nuclear staining was achieved at 3.48% acetic acid (pH 2.47), with pH deviations compromising staining quality. Tissue-specific differences in nuclear staining were observed: liver, prostate, and lung stained better than stomach, thyroid, and breast, and surgical specimens outperformed biopsies. Notably, the staining effect of overacidified Gill's hemalum could not be restored by adding sodium hydroxide (NaOH). At the optimal pH, Gill's hemalum surpassed Harris' hemalum in nuclear staining quality. Furthermore, the addition of a pH buffer not only improved staining consistency but also significantly extended the usable lifespan of Gill's hemalum. These findings indicate that the acetic acid concentration (pH) of Gill's hemalum is a key quality control parameter for H&E staining. Gill's hemalum prepared with 3.48% acetic acid and used with a pH buffer, offers significant practical value and is recommended for routine use in a pathology laboratory.
Autoimmune gastritis (AIG), a chronic inflammatory disease associated with various comorbidities and complications, is often subject to missed or delayed diagnoses. The objective of this study was to develop an artificial intelligence (AI) system to assist in the diagnosis of AIG by integrating multimodal information, including endoscopic images, biopsy pathological reports, and laboratory test results. Multimodal data were collected from 590 patients diagnosed with AIG, H. pylori positive, and H. pylori negative chronic atrophic gastritis at three medical centers. A multimodal AI system was developed to diagnose AIG and other types of chronic gastritis. The performance of the AI system was evaluated using both internal and external datasets. Six endoscopists were invited to perform three-category classification for comparison. The SHapley Additive exPlanations (SHAP) framework and EigenCAM were used to improve the interpretability of the AI system. The proposed multimodal AI system achieved a sensitivity of 0.931, specificity of 0.963, and accuracy of 0.954, significantly outperforming unimodal models (all P<0.001) and the best-performing invited expert endoscopist under controlled conditions. SHAP analysis indicated that the three modalities provided complementary and synergistic information. This multimodal AI system has the potential to enable practical AI-assisted diagnosis of AIG with exceptional accuracy.
Velopharyngeal dysfunction (VPD) is an impaired ability to achieve adequate velopharyngeal closure during speech, often resulting in hypernasality and reduced intelligibility. VPD screening and diagnosis require specialized expertise and controlled recording conditions, limiting scalable access outside high-income countries.Key challenge: Speech-based machine learning models can perform extremely well under standardized clinical recording conditions. However, performance often deteriorates when deployed on consumer devices (e.g., phones or tablets) and in uncontrolled acoustic environments. This degradation is largely driven by domain shift arising from differences in recording conditions (e.g., device and channel characteristics, background noise, and room acoustics), which can cause models to rely on spurious recording artifacts rather than pathology-relevant cues. This study introduces a two-stage framework to improve robustness under realistic recording scenarios. Nasality representation pre-training employs a nasality-focused representation via supervised contrastive learning (SupCon) using an auxiliary dataset with phoneme alignments to form oral-context versus nasal-context supervision. During Frozen-encoder VPD screening, the encoder is frozen to perform VPD screening using lightweight classifiers on 0.5-second chunks with probability aggregation to produce recording-level decisions using a fixed decision threshold. Here, in-domain refers to standardized clinical recordings used for model development, and out-of-domain refers to heterogeneous public Internet recordings collected under uncontrolled conditions and evaluated without any adaptation. The proposed method is then compared against prior-study baselines, including MFCC features and large pretrained speech representations, using the same evaluation protocol. On the primary in-domain subject-disjoint held-out split of 82 subjects (60 train/22 test; 345 training recordings; 131 test recordings; multiple recordings per subject), the proposed approach reached ceiling recording-level screening performance under this standardized clinical protocol (macro-F1 = 1.000, accuracy = 1.000). To assess sensitivity to this fixed split, an additional subject-level nested 5-fold cross-validation analysis was performed on the full in-domain cohort (82 subjects, 476 recordings), with the encoder frozen and only the second-stage classifiers retrained; the best mean performance was obtained with SVM (macro-F1 = 0.981 ± 0.022, accuracy = 0.985 ± 0.016). On a separate out-of-domain set of 131 public Internet recordings, large pretrained speech representations degrade substantially, and MFCC is the strongest baseline (macro-F1 = 0.612, accuracy = 0.641). The proposed method achieves the best overall out-of-domain performance (macro-F1 = 0.679, accuracy = 0.695), improving over the strongest baseline by +0.067 macro-F1 and +0.054 accuracy (point-estimate improvements) under the same evaluation protocol and fixed threshold. Learning a nasality-focused representation prior to clinical classification can reduce sensitivity to recording artifacts and improve robustness when moving from the laboratory to real-world audio recording scenarios. This design supports practical deployment of VPD screening and motivates domain-robust evaluation protocols for deployable speech-based digital health tools.
Iron-deficiency anemia is challenging to diagnose in patients with cyanotic congenital heart disease (CCHD) because of the high hemoglobin concentration as a compensatory mechanism for cyanosis. This study aimed to identify predictive parameters of iron depletion status and assess the prevalence of iron deficiency in patients with CCHD. A descriptive study enrolled patients with CCHD aged 6 months to 15 years between September 2022 and September 2023. Participants were categorized into iron depletion and iron sufficiency groups defined by a serum ferritin level <30 ng/mL or transferrin saturation <15%. The clinical characteristics and laboratory parameters were compared between groups. The Youden index was used to determine the optimal cutoff points, maximizing both sensitivity and specificity for the red cell indices. Among the 110 enrolled patients with CCHD, 28 (25.5%) had iron depletion. While hemoglobin (Hb) and hematocrit (Hct) levels were comparable between groups, the iron-depleted group exhibited significantly lower reticulocyte hemoglobin content (CHr) and higher red cell distribution width (RDW). A multivariate analysis identified an RDW ≥18.8% (odds ratio [OR], 7.79; P=0.001) and CHr <28 pg (OR, 4.97; P=0.004) as independent predictors of iron depletion. The diagnostic accuracy improved to 85.4% when these parameters were combined. Iron depletion is highly prevalent in patients with CCHD. Because conventional parameters, such as Hb and Hct, fail to differentiate iron status owing to compensatory mechanisms, CHr and RDW serveas effective predictive tools. This combination offers a practical diagnostic alternative, especially in resource-limited settings, where standard iron studies are unavailable.
Introduction The global rise of multidrug-resistant Acinetobacter (A.), particularly A. baumannii, is well recognized. However, real-world data linking subspecies distribution with resistance patterns and polymicrobial dynamics in real-world tertiary care settings remain limited. This study addresses this gap by providing a comprehensive and clinically relevant profile of Acinetobacter isolates, emphasizing species-specific resistance patterns and polymicrobial associations.  Methods A prospective cross-sectional study was conducted over six months (July-December 2023) in a tertiary care teaching hospital in South India. Clinical specimens yielding Acinetobacter species, including polymicrobial cultures, were analyzed. Antimicrobial susceptibility testing (AST) was performed using VITEK 2 COMPACT (BioMérieux, Marcy-l'Étoile, France) and Kirby-Bauer according to Clinical and Laboratory Standards Institute (CLSI) 2022 guidelines. The minimum inhibitory concentration (MIC) for polymyxin B was determined using microbroth dilution. Data were analyzed using Statistical Package for the Social Sciences (SPSS) Statistics version 21 (IBM Inc., Armonk, New York). Results Among 181 isolates, A. baumannii was the most prevalent, 118 (65.2%), followed by A. lwoffii, 39 (21.5%), and A. haemolyticus, 24 (13.3%). A strong predominance was observed in the intensive care unit (ICU), which accounted for 143 (79.0%) isolates. A key finding was the clear variation in resistance profiles across subspecies. A. baumannii demonstrated very high multidrug‑resistant (MDR), 102 (86.4%), and extensively drug‑resistant (XDR) 16 (13.6%) rates, whereas non‑A. baumannii species exhibited substantially lower resistance levels. Carbapenem resistance remained alarmingly high, with meropenem resistance at 145 (80.1%) and imipenem resistance at 124 (68.5%) across both ICU and non‑ICU settings, suggesting significant institutional selection pressure. Despite this, susceptibility to polymyxin B, 168 (92.8%), and tigecycline, 172 (95.0%), remained largely preserved. Polymicrobial infections were identified in 28 (15.5%) cases and showed a significant association with species type, with A. lwoffii exhibiting higher co‑isolation rates than A. baumannii. The most common co‑pathogens were Pseudomonas and Klebsiella species. Conclusion This study demonstrates clear species-level differences in both resistance and polymicrobial behavior within Acinetobacter, emphasizing the clinical importance of routine subspecies identification. The combination of high carbapenem resistance across care settings and species-specific polymicrobial trends highlights the need for precision antimicrobial stewardship, early targeted therapy, and unit-specific antibiograms. Together, these findings offer practical evidence to guide improvements in empiric treatment strategies in high-risk hospital settings.
The six sigma model is widely applied in laboratory quality management. This study used total allowable error (TEa) based on National Center for Clinical Laboratories (NCCL) external quality assessment (EQA) criteria and "desirable" biological variation (BV) specifications as quality goals to evaluate the analytical performance of 12 cytokines in five laboratories and develop individualized quality control (QC) strategies. Imprecision and trueness data for 12 cytokine assays were collected from five laboratories and used to calculate sigma metrics. Assay performance was visualized using a normalized sigma method decision chart. The Westgard sigma rules run-size flowchart and quality goal index (QGI) were further applied to design customized QC protocols and identify priority areas for improvement. Sigma metrics varied by concentration level, with higher-concentration materials generally yielding better sigma performance. For the same analyte, sigma values also differed according to the selected quality goal. Compared with "desirable" BV specifications, NCCL criteria produced lower sigma values for interleukin-6 (IL-6), whereas interferon-γ (IFN-γ) and tumor necrosis factor-α (TNF-α) showed the opposite trend. These differences were clearly demonstrated by the normalized sigma method decision chart. Based on Westgard sigma rules and QGI, individualized QC strategies and targeted improvement measures were established for assays with sigma values below 6. The six sigma model provides a practical framework for cytokine assays quality management, supporting targeted QC design and focused analytical performance improvement.
Reliable individual identification is essential for long-term tracking and reproducible laboratory animal studies. In amphibians, invasive marking methods like tags and dye injections can cause welfare concerns and may be unreliable because of tag loss or migration. We developed and evaluated a non-invasive identification system for 25 adult Pleurodeles waltl using smartphone-captured images and the pre-trained convolutional neural network EfficientNetV2. To determine the most informative imaging region, separate models were trained and tested using head and whole-body images. The head-image model achieved 95.3% accuracy on the independent test dataset (macro F1-score = 0.946; Cohen's kappa = 0.951), markedly outperforming the whole-body model (56.6% accuracy). Grad-CAM visualization showed that the model primarily focused on dorsal head spot patterns, indicating that these markings are more informative for individual recognition than whole-body patterns. Because this method requires only a smartphone and a trained model, it can be implemented without specialized marking equipment. This approach enables accurate individual identification, while avoiding invasive marking and therefore supports both animal welfare and research reproducibility. It may provide a practical basis for standardizing individual identification in future amphibian research.