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To evaluate whether serum markers of malnutrition were associated with postoperative complications after tibial shaft fracture fixation, and whether postoperative correction mitigates this risk. Design: Retrospective cohort study. Multicenter (50 healthcare centers; North American national database). Adult patients &#x2265;18 years of age undergoing fixation of tibial shaft fractures (OTA/AO 42) between January 1, 2013, and December 31, 2022, with a minimum of one year of follow-up were included. Patients with a history of malignancy or Gustilo-Anderson type III open fractures were excluded from the study. The primary outcome was the occurrence of postoperative complications. Patients with serum markers for malnutrition were compared to matched controls. Malnourished patients with subsequently normalized serum markers were compared to previously matched healthy cohorts and cohorts with persistent malnutrition. Following matching, a total of 10,722 patients were included for primary analysis: 5,361 malnourished patients with mean age 52 (range 18-90), 53.6% male and 5,361 control with mean age 53.2 (range 18-90) with 52.9% male. Preoperative malnutrition was associated with significantly higher rates of postoperative complications, including nonunion (RR=1.4), revision surgery (RR>1.4), infection (RR>2.0), failure of fixation (RR=1.3), thromboembolic events (RR>2.0), and sepsis (RR>2.0) (all p < 0.001). Among patients with preoperative malnutrition, those with uncorrected postoperative serum markers demonstrated higher risks of nonunion, revision, infection, and implant failure compared to those with serum markers that were corrected postoperatively (RR>2.0, all p<0.001). However, when compared to patients with normal preoperative nutrition values, individuals with postoperatively corrected malnutrition remained at increased risk for revision and infection (RR>1.4, all p=0.002) but not nonunion (6.5% vs. 5.6%, p>0.05) or failure of fixation (4.6% vs. 4.1%, p>0.05). Malnutrition was associated with adverse surgical outcomes following tibial shaft fracture fixation, with significantly higher rates of nonunion, revision procedures, infection, wound complications, thromboembolic events, and medical morbidity compared with patients with normal preoperative nutrition. Postoperative correction of nutritional deficiencies reduced complication risk; however, residual risk remained elevated relative to patients with normal preoperative nutritional markers. These findings highlight malnutrition as a partially modifiable risk factor in tibial shaft fracture repair and support nutritional optimization as a part of postoperative management to improve patient outcomes. Level III.

Ziehl-Neelsen (ZN) smear microscopy remains central to tuberculosis (TB) diagnosis and treatment monitoring; however, its sensitivity is limited by incomplete recovery of Mycobacterium tuberculosis during pre-analytical processing. This study evaluated whether modifying centrifugation force and duration improves bacillary recovery and ZN smear microscopy performance. Laboratory experiments were conducted using M. tuberculosis H37Rv suspensions and clinical sputum specimens. Following NALC-NaOH treatment, samples were centrifuged at 2,000, 3,000, and 6,000&#x2009;&#xd7;&#x2009;g for 40 min. The effect of centrifugation duration was assessed at 3,000&#x2009;&#xd7;&#x2009;g by comparing 20 and 40 min using the same M. tuberculosis H37Rv cultures and the same clinical sputum specimens at both time points, ensuring paired measurements within each sample type. Smear positivity and ZN smear grading were evaluated from replicate smears and analyzed using non-parametric statistical tests, with significance set at p&#x2009;<&#x2009;0.05. In M. tuberculosis H37Rv suspensions, no significant differences in smear positivity or grading were observed across centrifugal forces (p&#x2009;=&#x2009;0.368 and p&#x2009;=&#x2009;0.212, respectively). In clinical sputum specimens, smear positivity did not differ significantly across forces (p&#x2009;=&#x2009;0.716), whereas ZN smear grading increased significantly with higher centrifugal force (p&#x2009;=&#x2009;0.0051). At 3,000&#x2009;&#xd7;&#x2009;g, extending centrifugation time from 20 to 40 min did not significantly affect smear positivity in either sample type (both p&#x2009;=&#x2009;1.000). In contrast, ZN smear grading increased from 1+ to 2+ in clinical specimens with extended centrifugation time (p&#x2009;=&#x2009;0.016), while no change was observed in M. tuberculosis H37Rv suspensions. These findings indicate that increasing centrifugal force may enhance bacillary concentration in clinical sputum, resulting in improved smear grading without a corresponding increase in detection rate. Extending centrifugation time has limited impact on smear positivity. Optimization of pre-analytical centrifugation parameters may improve ZN smear microscopy performance in clinical specimens.

Brain tumors are one of the most fatal disorders that cause one of the highest mortalities in the world. Gliomas are the most common primary brain tumors originating from glial cells in the central nervous system. Traditionally, a tissue sample is extracted and examined for its genetic and characteristic properties. This method is invasive, painful, and takes a longer period to produce results. Various automatic Deep learning (DL) based schemes have been presented for the brain glioma detection, but they lack due to poor explainability, lower generalization, poor feature depiction, class imbalance problem and lower detection rate. This paper presents a deep learning based brain tumor detection using two way feature depiction model (TWFDM) that combines the 2D-Deep Convolution Neural Network (DCNN) and 1D-DCNN. The 2D-DCNN accepts the raw MRI images and the 1D-DCNN accepts the handcrafted local binary pattern (LBP), gray level cooccurrence matrix (GLCM), and Histogram of Oriented Gradient (HOG) features. Furthermore, improved particle swarm optimization (IPSO) is used for feature selection to minimize the computational complexity of the TWFDM system. The proposed TWFDM achieves an overall accuracy of 96.25%, a recall of 96.34%, a precision of 96.31%, and an F1-score of 96.32% on the Brain MRI dataset for four-class classification, representing an important improvement over traditional techniques.

Escherichia coli O157:H7 is a highly virulent strain of E. coli that can cause food poisoning and infections such as hemolytic uremic syndrome, even in small quantities. To prevent and mitigate outbreaks, the rapid and sensitive detection of E. coli O157:H7 is crucial. Although antibodies have been widely used as biorecognition elements for bacterial detection, their high cost and cross-reactivity cause considerable frustration among researchers, calling for more cost-effective and specific bioprobes. Here, we engineered bacteriophage tail spike proteins (TSP) by eliminating active sites for strong binding and established a TSP-based magnetic-assisted luminescence assay (T-MALA) for the specific detection of E. coli O157:H7 cells. In the T-MALA, we utilized TSP-conjugated Dynabeads for capturing and nanoluciferase-fused TSP as a detection probe. After magnetic separation, the cell-bound Nluc-mTSPs generated luminescence upon exposure to its substrate, furimazine, enabling the detection of E. coli O157:H7 cells. After optimization, the entire process was completed in less than 1 h, with a low experimental detection limit (LOD) of 20 CFU/mL, and showed high specificity for E. coli O157:H7 strains. This assay exhibited a robust linear correlation between the luminescence values and bacterial concentrations ranging from 30-105 CFU/mL. Furthermore, T-MALA was able to detect E. coli O157:H7 cells in various food matrices, such as milk, lettuce, and ground beef, which are commonly associated with E. coli O157:H7 contamination. These results demonstrate the potential of T-MALA as an alternative strategy for rapid detection of E. coli O157:H7.

The aim of the study was to enhance understanding of how proactive deprescribing can be implemented in primary care. This study explored the views of healthcare professionals (HCPs) on the normalization of safe and routine deprescribing in English primary care. An interview guide was developed from deprescribing literature and underpinned by Normalization Process Theory. Interviews and focus groups were conducted with general practitioners, primary care pharmacists, community pharmacists, and clinical commissioning group staff involved in medicines optimization. Focus groups were conducted online, while interviews were conducted via telephone or online using Microsoft Teams&#xae;. Focus groups and interviews were transcribed verbatim, and data were analysed using Framework analysis to generate themes. Thirty participants (57% female), consisting of general practitioners (n&#x2009;=&#x2009;9), community pharmacists (n&#x2009;=&#x2009;6), primary care pharmacists (n&#x2009;=&#x2009;14), and a clinical commissioning group staff member (n&#x2009;=&#x2009;1), were recruited to three online focus groups, eight online interviews, and one telephone interview. Three themes were developed: (i) 'Current deprescribing climate' highlighted factors promoting pressure to prescribe rather than deprescribe; (ii) 'Routine implementation, roles and responsibilities' emphasized the role of patients and pharmacists in routine deprescribing; (iii) 'Keeping deprescribing safe' identified strategies for maintaining safety during deprescribing. Deprescribing normalization can be strengthened by leveraging cognitive participation (engagement work) and collective action (the work required to implement the intervention). Normalizing deprescribing is challenging due to entrenched prescribing habits, safety concerns, and legal uncertainties. HCPs' personal experiences shaped confidence in deprescribing. Participants emphasized the importance of structured guidelines and teamwork in embedding deprescribing into practice. Expanding roles, establishing clear protocols, and fostering collaboration were viewed as essential for safe implementation.

Lassa fever is a severe, often fatal febrile illness endemic to West Africa caused by Lassa virus (LASV), with different virus lineages predominating across West African countries. The viral nucleoprotein (NP) is a target antigen for serological assays to identify previous exposure to LASV. To our knowledge, there is no commercially available assay that reliably quantifies anti-LASV-NP IgG antibodies in human serum. We report the development and qualification of an ELISA designed to detect and quantify anti-LASV-NP IgG in human serum samples. Following assay optimization, performance was assessed through assay qualification at clinical trial laboratories within Ghana. Assay positivity criteria, lower limit of detection, upper and lower limits of quantification, inter-assay precision, selectivity and dilutional linearity were determined. A new reference standard prepared from pooled sera from donors in endemic Lassa fever regions was established and calibrated to the first WHO international standard for LASV antibodies. One ELISA assay utilizing lineage IV LASV-NP was applicable for detection of anti-LASV-NP IgG antibodies in serum samples from different West African countries where either LASV lineages I, II, III and IV predominate. The ELISA remained selective in hemolysed serum samples with minimal loss of signal across repeated sample freeze-thaw cycles. Crucially, the developed ELISA was fully concordant with a now discontinued commercially available ELISA kit for quantification of anti-LASV-NP antibodies. Our anti-LASV-NP IgG ELISA was shown to reliably measure anti-LASV-NP IgG levels in human serum. Establishing and conducting this assay within West Africa represents an essential step towards strengthening LASV epidemiology research and supporting urgently needed development of a vaccine to prevent Lassa Fever.

Ultrasound education has traditionally relied on on-site training, but scalable digital solutions are increasingly needed. This study aimed to evaluate the effectiveness of a simulation-based online ultrasound platform compared with traditional on-site training. A prospective randomized blinded study was conducted with 68 physiotherapy students (n=34 per group). Participants were assigned to a simulation-based online training platform (WAZO) or traditional on-site instruction. Both groups completed identical theoretical and practical assessments. Item response theory using a Rasch model was applied to evaluate item difficulty and student ability. No significant differences were found between the online and on-site groups in theoretical scores (mean 4.94, SD 1.47 vs mean 5.08, SD 1.14; P=.65) or practical performance variables, including probe handling (26/34, 76.5% vs 28/34, 82.4%; P=.37) and structure identification (24/34, 70.6% vs 22/34, 64.7%; P=.19). Measurement outcomes also showed no significant differences, including structure diameter (mean 3.78, SD 0.79 mm vs mean 3.98, SD 1.27 mm; P=.46) and structure surface distance (mean 3.94, SD 1.97 mm vs mean 3.24, SD 0.64 mm; P=.06). Item response theory analysis identified image optimization, structure diameter, and structure surface distance as the most difficult items, while probe handling and structure identification were the most informative. The model demonstrated high discriminative performance (area under the curve=0.93), with sensitivity of 87% and specificity of 80%. Simulation-based online ultrasound training provides comparable theoretical and practical outcomes to traditional on-site instruction, supporting its use as a scalable and accessible educational alternative.

Rutin (Rn) and luteolin (Lu), as important plant-derived natural compounds, hold significant value in the fields of medicine, nutrition, and food science. However, their highly similar structures often lead to challenges such as signal overlap and poor selectivity in traditional electrochemical detection methods. To address these issues, this study developed an intelligent electrochemical sensing platform that integrates nanocomposites and machine learning. A ternary heterojunction structure of biochar (CB)/ZIF-8/MnIn2S4 was employed to synergistically enhance electron transfer and catalytic efficiency. Combined with the random forest algorithm, the platform enabled machine learning-assisted feasibility assessment of target analytes, optimization of experimental parameters, and precise concentration prediction. The sensor demonstrated a wide linear range for Rn (0.01-500&#x202f;&#x3bc;M) and Lu (0.29-784.8&#x202f;&#x3bc;M), low detection limits (Rn: 3.48&#x202f;nM, Lu: 2.41&#x202f;nM), and high sensitivity (5.54&#x202f;&#x3bc;A&#x202f;&#x3bc;M-1&#x202f;cm-2). Furthermore, the random forest model achieved high-precision mapping of signal-concentration relationships, with a high classification accuracy. In real sample analysis (honeysuckle, ginkgo leaf extracts, and human serum), the recovery rates ranged from 96.4% to 105.2%, with an RSD of less than 3.72%, consistent with HPLC results. This work provides a reliable data-driven sensing approach for the quality control of natural product active ingredients and with potential for clinical monitoring of blood drug concentrations.

The use of super absorbent polymers in agriculture for water and fertilizers retention in soils has become popular with the increasing need for resource optimization. The objective of the present study was to use chitosan extracted from shrimp shell waste and reagent grade carboxymethylcellulose to synthesize a biodegradable super absorbent polymer with potential use for soil amendment in agriculture. The super absorbent polymer was synthesized using epichlorohydrin as a crosslinking agent in an alkaline NaOH/urea medium. The structure of the product was confirmed by FTIR and TGA. The polymer was found to be biodegradable with a progressive weight loss percentage reaching 79.1% after 14 days. An adsorption ratio of 15.8 and 17.2 was obtained for water and 22% w/v urea solution, respectively, so the product was categorized as super absorbent in both conditions. In addition, after two hours in the medium, absorption percentages of 48.3% were recorded for water and 22% w/v for urea solution. The reported method is effective for synthesizing a biodegradable super absorbent polymer with potential use for soil amendment and susceptibility to pH changes for both adsorption equilibrium and over time adsorption.

Lotus seed peel powder (LSP), a major by-product of lotus seed processing, is rich in bioactive constituents but remains largely underutilized. Conventional aqueous and ethanolic extraction methods for LSP are constrained by low extraction efficiency and poor selectivity. In this study, an ultrasound-assisted deep eutectic solvent (DES) extraction (UADE) strategy was developed to recover tyrosinase inhibitors from LSP. UADE significantly enhanced flavonoid and polyphenol extraction compared with traditional methods. Among the tested DESs, the L-proline/lactic acid system exhibited the highest selectivity for tyrosinase inhibitors. Using response surface methodology, the optimal extraction conditions were determined as follows: Pro to LA 1:2, 20&#xa0;% water, solid-to-liquid ratio 1:40 (w/v), 47&#xa0;&#xb0;C, 300&#xa0;W, 70&#xa0;min. Under these conditions, the tyrosinase inhibition rate reached 96.51&#xa0;%, and the half-maximal inhibitory concentration (IC50) of the purified extract was 1.02&#xa0;mg/mL. In addition, the DES system retained good reusability over multiple extraction cycles. Mechanistic analyses revealed that ultrasonic treatment markedly disrupted LSP cell wall structure, promoting the release of active components. Four key tyrosinase inhibitors, including isorhamnetin-3-O-galactoside-6''-rhamnoside, were identified via UHPLC-QE-Orbitrap-MS analysis combined with molecular docking and molecular dynamics simulations. DFT calculations suggested that hydrogen bonding and &#x3c0;-&#x3c0; stacking interactions between DES components and key inhibitors were the core driving forces of extraction selectivity. Zebrafish assays confirmed that the extract inhibited tyrosinase activity in vivo and downregulated the expression of multiple melanogenesis-related genes. These findings establish UADE as an effective and selective approach for the extraction of tyrosinase inhibitors from natural resources, providing a methodological basis for developing anti-melanogenic cosmetic ingredients.

Acute liver injury (ALI) is a syndrome characterized by rapid deterioration of liver function, rapid progression, and high mortality. In this study, a liver-targeted drug delivery system, galactosylated chitosan modified Coreopsis tinctoria flavonoid liposome (GC-CTF-Lip) was constructed. The targeted ligand was synthesized by the amidation reaction between chitosan and lactobionic acid, and its structure was identified. The modification conditions of GC on liposomes were optimized and characterization of liposomes was studied. The liver-targeting ability of GC-CTF-Lip was evaluated through in vivo and in vitro experiments. A mouse model of ALI induced by CCl4 was established to evaluate the hepatoprotective effect of GC-CTF-Lip. The results confirmed that lactobionic acid was successfully grafted onto chitosan. Finally, GC-CTF-Lip with particle size of 236.32&#x2009;&#xb1;&#x2009;0.60&#x2009;nm and encapsulation efficiency of 68.67&#x2009;&#xb1;&#x2009;0.58% was obtained. GC-CTF-Lip improved the uptake efficiency of CTF in hepatocytes in vitro and exhibited excellent liver enrichment in vivo. Serum ALT/AST levels were significantly decreased, and the histological liver injury was alleviated. It exhibited a trend of liver enrichment and a hepatoprotective effect. This liver-targeted nanodrug delivery system can achieve active accumulation of drugs at the lesion site, providing a potential strategy for the prevention of ALI.

Stable mounting is a central requirement for skin-interfaced wearable biomedical devices, because accurate and long-term measurements with clinical utility typically demand intimate contact with the skin, whereas practical use also requires gentle removal to minimize skin irritation and damage. Existing mounting strategies often struggle to satisfy these competing requirements simultaneously, especially under prolonged wear or in the presence of sweat and moisture. Suction-based mounting has recently emerged as a promising alternative because it can provide strong, reversible, and adhesive-free attachment, yet its underlying mechanics remain insufficiently understood. Here, we establish analytical models for the deformation and force of suction cups in a fully explicit form, covering both the cone suction cup and an optimized ring suction cup design. Unlike previous approaches that rely on indirect quantities such as the pressure difference and contact radius, which are not available before experiments and therefore cannot serve as controllable design variables, the present framework yields direct relations between suction performance and geometry parameters, material properties, and loading conditions, including the maximum push down displacement and the subsequent pull up displacement. The resulting formulas agree closely with accurate numerical solutions and lead to compact scaling laws that clearly identify how geometry and material parameters govern suction performance. These results provide a quantitative and physically transparent foundation for the design of suction-based mounting strategies in wearable devices.

Post-traumatic stress disorder (PTSD) is a debilitating psychiatric condition characterized by hyperarousal, intrusive memories, and impaired fear extinction, often accompanied by persistent sleep disturbances. The orexin (hypocretin) system, which regulates arousal, stress reactivity, and REM sleep, has emerged as a promising therapeutic target in PTSD. This review summarizes preclinical and limited clinical evidence examining the effects of orexin receptor antagonists, especially suvorexant, on PTSD-relevant behaviors and neurobiology. This narrative review evaluates data from 11 preclinical and clinical studies that investigated the effects of dual (DORA) or selective orexin receptor antagonists on PTSD-like phenotypes. Animal studies involved established stress models, including single prolonged stress (SPS), predator scent stress, fear conditioning/extinction paradigms, and stress-alternatives models. Behavioral outcomes included freezing behavior, anxiety-like behavior, fear extinction retention, and REM sleep modulation. Some studies investigated molecular markers, such as orexin-A levels, corticosterone, CRF-R1, serotonin, mitochondrial fission/fusion proteins, and mTOR signaling. One double-blind clinical trial of suvorexant was also included. IRB approval was not required for this review. Across studies, orexin antagonism consistently reduced PTSD-like behaviors. Suvorexant improved REM sleep and accelerated fear extinction in mice, even following circadian disruption. Orexin receptor-1 antagonists (e.g., SB334867) facilitated fear extinction and decreased freezing behavior via basolateral amygdala mechanisms. In stress-exposed rodents, suvorexant reversed hyperarousal, avoidance, and anxiety-like behaviors, attenuated elevated orexin-A and CRF-R1 levels in the amygdala, and restored HPA axis function. Mitochondrial dysfunction and mTOR activation, observed in rodent models of PTSD, were normalized with suvorexant treatment, suggesting cellular resilience. One clinical trial of suvorexant in trauma-related insomnia found REM enhancement and nightmare remission in most patients, although a strong placebo response limited group-level findings. Notably, reduced orexin signaling was associated with resilience in multiple models. The reviewed evidence supports orexin receptor antagonism-particularly via dual antagonists like suvorexant-as a promising therapeutic approach for PTSD, with benefits across numerous domains: fear extinction, sleep regulation, hyperarousal, and cellular stress resilience. These findings suggest that orexinergic overactivation contributes to PTSD pathophysiology and that DORAs may offer an advantage over traditional sedatives by enhancing REM sleep and modulating stress signaling. Limitations include a predominance of preclinical data and heterogeneity in experimental models. Nonetheless, these studies provide a mechanistic foundation for larger clinical trials, particularly in patients with treatment-resistant PTSD or prominent sleep disturbance. Future research should explore receptor-specific roles, optimal timing of intervention, and biomarkers predictive of treatment response.

This work presents a compact dual-band millimeter wave (mmWave) antenna that operates at 17-27 GHz and 36-43 GHz, with resonance frequencies centred at 21.1 GHz and 38.3 GHz, respectively. The multiple embedded arc-shaped mmWave antenna geometry provides compact, effective dual-band operation. The concentric arcs of the antenna structure are effectively optimized to produce a dual band of operation with good impedance matching. The resulting antenna has gain of 4.8 dBi at 26 GHz and 6.1 dBi at 37 GHz frequencies. The developed antenna is intended for widespread usage in satellite and 5G networks. Despite providing significant bandwidth for 5G and other next-generation communication systems, mmWave frequencies are susceptible to path loss, particularly in congested urban areas. This study further investigates the performance of the designed dual-band mmWave antennas in various urban environments. Both Line-of-Sight (LOS) and Non-Line-of-Sight (NLOS) scenarios are considered to capture realistic deployment conditions. To accurately model propagation behaviour, the analysis incorporates the ABG and CI path-loss models, which comprehensively account for environmental factors such as shadowing, diffraction, and precipitation.

With the advances in neonatal care over the last three decades leading to improved survival from preterm birth, it is becoming increasingly evident that preterm-born individuals experience respiratory morbidity and reduced lung function through the life course, now termed prematurity-associated lung disease (PLD), with increasing concern regarding the onset of chronic obstructive pulmonary disease in early adult life. In this article, we will cover the current evidence for screening, monitoring and management of PLD. We shall discuss monitoring strategies utilizing lung function testing, lung imaging, and oximetry, amongst others. Although current data regarding optimal management and treatment are limited, we shall discuss pharmacological and non-pharmacological methods currently for PLD. It is now clear that long-term respiratory follow-up for high-risk preterm-born individuals is imperative to identify PLD early, monitor its progress and optimize respiratory outcomes. There must be increased recognition from pediatric and adult physicians of the impact of prematurity and low birth weight on respiratory health in later life, with services developed to transition individuals with PLD from pediatric to adult care. Future research should aim to improve understanding of PLD phenotypes and phenotype-targeted interventions to optimize respiratory health in PLD across the life course.

Conventional biomarkers such as estimated glomerular filtration rate (eGFR) and urinary albumin-to-creatinine ratio (uACR) primarily reflect glomerular damage and often fail to detect early tubular injury. Consequently, patients with "non-albuminuric diabetic kidney disease (DKD)" may be overlooked. This study evaluated the independent association between urinary post-translationally modified fetuin-A fragments (uPTM-FetA) and DKD risk stratification in Japanese patients with type 2 diabetes. We conducted a cross-sectional study of 219 outpatients with type 2 diabetes between November 2023 and February 2024 at Edogawa Hospital. First-morning urine samples were analyzed for uPTM-FetA and urinary liver-type fatty acid-binding protein (uL-FABP) using enzyme-linked immunosorbent assays. DKD risk was classified into four categories based on the KDIGO guidelines. The association between uPTM-FetA and higher DKD-risk (categories 2&#x2009;+&#x2009;3&#x2009;+&#x2009;4) was assessed using multiple logistic regression and restricted cubic spline (RCS) analyses, validated by bootstrapping. The optimal cutoff value for uPTM-FetA was determined to be 11.76 ng/mgCr. Multivariable analysis adjusted for potential confounders revealed that high uPTM-FetA levels were significantly and independently associated with DKD-risk categories 2&#x2009;+&#x2009;3&#x2009;+&#x2009;4 (adjusted odds ratio: 3.88; 95% CI: 2.02-7.45; P&#x2009;<&#x2009;0.01). RCS analysis indicated a significant non-linear association (P&#x2009;=&#x2009;0.04). Notably, high uPTM-FetA was detected in 38.8% of patients with normoalbuminuria and 42.0% of those with preserved eGFR. A striking discrepancy was observed compared to uL-FABP: while high uL-FABP was completely absent (0.0%) in patients within the low-to-moderate risk categories (categories 1 and 2), high uPTM-FetA was observed in 34.0% and 60.8% of these patients, respectively. uPTM-FetA is independently associated with DKD severity and is elevated in a substantial proportion of patients with early-stage disease where conventional markers remain normal. Unlike uL-FABP, which increases predominantly in advanced stages, uPTM-FetA appears to identify tubular stress earlier. Thus, uPTM-FetA serves as a valuable complementary biomarker to uACR for refining DKD risk stratification.

Inequitable and time-consuming shift scheduling contributes to nurse burnout, dissatisfaction, and turnover. In Taiwan, annual nurse turnover reaches 11.6%, with rigid 3-shift systems and unfair workload distribution frequently cited as key drivers. Although artificial intelligence (AI) scheduling tools exist, most lack transparency and do not formally address algorithmic bias, limiting clinical adoption. This study aimed to design, deploy, and evaluate a transparent, fairness-audited, explainable AI-enabled nurse scheduling decision support system (XAI-NSDSS) to reduce administrative burden, eliminate experience-based algorithmic bias, and enhance staff acceptance in a real-world hospital setting. A pragmatic before-after implementation study was conducted at a 671-bed teaching hospital in Taiwan (January-December 2023), involving 8 departments and 156 nurses (42 novice, 78 midlevel, and 36 experienced). A 6-month manual scheduling baseline (January-June 2023) was compared with a 6-month AI-assisted period (July-December 2023). The XAI-NSDSS integrates a random forest workload prediction model (R&#xb2;=0.887), Shapley Additive Explanations-based explainability, a hybrid integer programming and binary differential evolution (IP+ BDE) optimizer, and a multidimensional fairness monitoring dashboard. A formal weight sensitivity analysis (WSA) was conducted across 7 prespecified weight configurations using full-factorial repeated-measures ANOVA to assess outcome robustness. Primary outcomes were scheduling time, error rate, and user satisfaction. Statistical analyses used linear mixed effects models (LMMs) and generalized estimating equations (GEE) with department as a random effect. Monthly scheduling time decreased by 81.2% (mean 32.0, SD 8.0-mean 6.0, SD 2.0) hours; P<.001; Cohen d=4.33) and error rate decreased by 73.8% (mean 18.3, SD 4.3%-mean 4.8, SD 1.2%; P<.001; Cohen d=4.12). Nurse satisfaction improved from a mean of 3.2 (SD 0.8) to a mean of 4.4 (SD 0.6; P<.001), with 148 out of 156 nurses (94.9%) adopting the system by Month 3. Preexisting experience-based bias was fully eliminated: workload coefficient of variation (CV) decreased 50% (0.18-0.09; P<.001), disparate impact ratios normalized from 1.35-1.56 to 1.01-1.04, and preference satisfaction equity was achieved across experience tiers (ANOVA P=.38). Among 156 nurses, 82 (52.6%) regularly engaged with Shapley Additive Explanations; this engagement was positively associated with satisfaction (Pearson r=0.456; P<.001). The WSA across 7 configurations confirmed that the consensus-derived default weights achieved the highest composite quality score (mean 82.1, SD 3.2) and that disparate impact ratios remained within the 0.80-1.25 fairness threshold across all configurations (P=.12), demonstrating structural robustness of the fairness-auditing module. This study presents the first longitudinally validated explainable AI implementation framework for nurse scheduling with formal algorithmic fairness auditing and WSA. The XAI-NSDSS framework is replicable, scalable, and provides a practical blueprint for responsible AI adoption in health care workforce governance, with fairness guarantees that are robust to institutional customization of optimization priorities.