This study aimed to explore clinicians' experiences during a ransomware attack at a public academic hospital in South Africa and assess the perceived impact of chemical pathology laboratory service disruptions on patient care. A cross-sectional survey was conducted between September and December 2024. An electronic questionnaire was distributed to clinicians to gather data on their experiences during the ransomware attack, including impacts on patient care and workload. To assess changes in test requesting practices during this period, volume data for both critical (creatinine) and non-critical (vitamin B12) tests from routine annual laboratory reports were analysed. Among the 58 respondents, 84% reported increased stress levels, while 78% indicated delayed diagnoses during this period. Laboratory test volumes decreased during the attack period compared to previous years, with reductions of 26.8% for creatinine and 34.1% for vitamin B12 tests. Clinicians primarily struggled with result retrieval and reported substantial disruptions to patient care. This study provides valuable insights into clinicians' perspectives on the impact of a laboratory ransomware attack. The findings highlight the critical need for investment in both cybersecurity infrastructure and comprehensive contingency planning to safeguard patient safety and minimise disruptions during future cyber incidents. This study addresses how ransomware attacks disrupt and impact clinician workflow in resource-limited hospital settings. Medical professionals should develop practical contingency plans for accessing and managing essential laboratory data during cybersecurity incidents to minimise care disruptions. The most significant finding was the dual impact of technical service disruption alongside pronounced clinician psychological stress, creating a compounded effect on healthcare delivery.
Polycystic ovarian syndrome (PCOS) is a multisystem disorder presenting with menstrual irregularities, infertility, and features of hyperandrogenism. Hyperandrogenism predisposes to the critical clinical features of PCOS. This find aimed to study the association of androgenic hormones such as dehydroepiandrosterone sulfate (DHEA-S) and free testosterone with lipid profile in PCOS women. This case-control study was conducted in the Department of Biochemistry at a tertiary care hospital in Chennai. Patients were recruited from the Department of Obstetrics and Gynecology. Participants were aged 18-40 years. Blood samples were collected for analysis of lipid profile, DHEA-S, and free testosterone. DHEA-S and free testosterone were analyzed by ELISA. Ethics approval and written informed consent were obtained. Based on the distribution of the data, appropriate statistical tools were used. P-value ≤ 0.05 was considered statistically significant. Most of the participants were aged between 21 and 30 years. HDL-c was decreased in PCOS patients compared to healthy individuals; however, no statistically significant difference was found. Free testosterone showed an association with triglyceride. The areas under the curves of DHEA-S and free testosterone were 0.638 and 0.765, respectively. DHEA-S and free testosterone showed good area under the curves. But free testosterone performed better with a higher area under the curve as well as its association with triglyceride. The cut-off values to diagnose PCOS were 3.0 μg/mL and 2.5 pg/mL for DHEA-S and free testosterone, respectively, with adequate sensitivity and specificity. Since free testosterone performed better in ROC curve than DHEA-S, free testosterone is considered to be a potential biomarker of identifying hyperandrogenism in PCOS women.
Reliable internal quality control (IQC) is vital for ensuring analytical accuracy in clinical laboratories. Conventional rule-based QC systems, such as Westgard and Levey-Jennings, often exhibit retrospective detection and limited sensitivity to small but clinically meaningful shifts. This study introduces the Triple-Point Pooled Sera (TriPPS) Quality Control system, a novel, machine-learning-based framework integrating in-house pooled sera with adaptive algorithms for enhanced error detection. Residual patient sera were pooled to create stable, matrix-relevant IQC material for 60 consecutive analytical days. Sodium and potassium were used as representative analytes. Three complementary machine learning models were applied: k-Nearest Neighbour (k-NN) for trend detection, Isolation Forest (IF) for random error identification, and Gaussian Process Regression (GPR) for systematic bias modeling. Controlled ±1% daily biases and stochastic random errors were introduced to simulate analytical drift. Detection lag, sensitivity, and anomaly classification were evaluated. The k-NN algorithm effectively identified trend errors within 0-2 days of bias onset, while IF accurately detected random fluctuations with minimal false positives. GPR modeled nonlinear systematic drift with high fidelity, capturing bias progression that is overlooked by linear methods. The integration of pooled sera enhanced the system's stability, reproducibility, and cost efficiency across all error types. The TriPPS system demonstrates a scalable, data-driven approach to laboratory quality control by combining pooled sera with machine learning algorithms. This framework enhances analytical vigilance, facilitates proactive error identification, and provides a practical, resource-efficient solution for real-time QC monitoring in clinical chemistry laboratories.
Clinical laboratories play a vital role in healthcare but contribute significantly to environmental challenges through high energy consumption, water usage, and waste generation. Pakistan's healthcare sector faces challenges, including limited funding and inadequate awareness of sustainable practices. There is little data on the extent to which clinical laboratories in Pakistan have implemented green practices, making it crucial to assess current efforts and identify barriers to adoption. This study aims to assess the adoption of sustainability and green lab practices in clinical laboratories across Pakistan. A cross-sectional survey was conducted by the Chemical Pathology section at Aga Khan University (AKU) using a structured questionnaire. The survey comprised 13 sections to evaluate sustainability practices, covering demographics, current green practices (energy efficiency, water conservation, waste management, etc.), barriers to implementation, environmental and cost impacts, and future goals. It assessed laboratories' existing efforts, challenges, and aspirations for improving sustainability. The survey was distributed via Google Forms to major laboratories across Pakistan via WhatsApp and email. Data was analyzed using Excel (Microsoft Corporation, 2018) software. A total of 12 laboratories across the country, from the capital Islamabad and all provincial capitals participated in the survey. Key findings include widespread adoption of energy-efficient lighting (75%) and electronic reporting (91.7%), but limited use of water-saving technologies (8.3%) and renewable energy (0%). Barriers like limited resources (58.3%), lack of staff awareness (50%), and financial constraints (41.7%) hindered green practices, though 41.7% reported moderate cost savings. Future goals focused on green certifications (58.3%), recycling programs (50%), and energy-efficient upgrades (41.7%). Our findings underscore the urgent need for structured sustainability policies, financial incentives, and educational programs to enhance green laboratory practices in Pakistan. While some progress has been made, significant gaps remain in energy efficiency, waste management, and regulatory compliance.
The thyroid gland undergoes physiological changes during pregnancy, leading to variations in reference intervals (RIs) for TSH and free thyroid hormones across the different trimesters. To ensure accurate interpretation of these tests, the American Thyroid Association (ATA) recommends the use of trimester-specific and population-based reference intervals. The aim of our study was: to establish trimester-specific reference intervals of TSH, FT3, and FT4 in pregnant women living in Oran western Algeria. The reference intervals were established accordingly to the CLSI guideline (EP28-A3c ). The study included 401 apparently healthy pregnant women, classified as follows: 120 in the first trimester, 154 in the second trimester, and 127 in the third trimester. Reference subjects were selected based on NACB exclusion criteria. Hormone assays were performed using the Roche Cobas e411 analyzer. The established RIs corresponding to the 2.5th and 97.5th percentiles were in the First, Second and Third trimester for TSH: 0.25-3.57 mIU/L, 0.15-3.43 mIU/L,0.57-4.23 mIU/L, FT4 : 11.41-20 pmol/L, 10.56-18 pmol/L, 9.43-17.54 pmol/L, and FT3 : 3.16-7.02 pmol/L, 3.02-7.54 pmol/L, 3.2-7.37 pmol/L. In the absence of population-specific reference intervals for TSH and thyroid hormones in our country, establishing such values represents a significant advancement, enabling more accurate diagnosis and improved management of thyroid disorders.
Monocyte Distribution Width (MDW) is the standard deviation of the mean volume of monocytes and may indicate innate immune activation. We investigated the possible association between MDW values and late HIV diagnosis in consecutive patients. We retrospectively enrolled newly diagnosed HIV patients admitted to our clinical center. Demographic and clinical characteristics were analyzed. A total of 97 patients were enrolled. Of these, 63% were late presenters and 43% fulfilled the criteria for advanced HIV disease. Continuous measures showed a significant inverse correlation between CD4 T-cell count and MDW. Multivariate analysis showed that MDW≥21.1 (OR:7.45, 2.13-30.54), HIV viral load >5 log (10) c/mL (OR:3.62, 1.04-13.30), blood lymphocytes<2 x103/μL (OR:14.82, 3.19-111.8) and HIV testing without symptoms (OR:0.21, 0.05-0.82) were independently associated with late presentation. Similarly, adjusted ORs for MDW≥22.5 (OR:4.03, 1.28-13.17), blood lymphocytes<1 x103/μL (OR:9.67, 2.19-57.57), age (OR:1.05, 1.00-1.10) and HIV testing without symptoms (OR:0.16, 0.04-0.52) were significantly associated with advanced HIV disease. Our results suggest that MDW may be a potential flagging parameter of innate immune activation in HIV infection. Continuous measurements of MDW showed a significant inverse correlation with CD4 T-cell count.Patients with increased MDW values were more likely to be diagnosed late.
Timely initiation of appropriate treatment of sepsis in the Emergency Department is crucial in a patient's prognosis. Surviving Sepsis Campaign guidelines suggest improved results in septic shock where antimicrobials are given within 1 hour of presentation. The recognition and treatment of critically ill patients could be expedited using different methods of point-of-care testing as opposed to centralised laboratory testing. C-reactive protein is utilised as a predictor for sepsis prognosis and should be interpreted in conjunction with patient clinical findings due to its low specificity and sensitivity. Some efficient and simple C-reactive protein point-of-care assay kits discussed include immunoturbidimetry, immunonephelometry, lateral flow assays and bioassays, which have advantages over laboratory-based methods, but still require further investigation. A useful tool such as a C-reactive protein point-of-care test could potentially enhance the operational performance of emergency care. Its use in optimizing antibiotic therapy to curb the rate of antimicrobial resistance and improve sepsis outcomes still requires validation. Septic shock management in the Emergency Department continues to be a challenging task and future verification studies and clinical guidelines on biomarkers for point-of-care testing are required to establish its reliability.
Breast cancer, a prevalent solid tumor, is the most common cancer among women worldwide. Hematological malignancies, such as acute myeloid leukemia, myelodysplastic syndrome, and acute lymphoblastic leukemia, are more frequent in breast cancer survivors, while plasma cell neoplasms are less common. We report the case of a 44-year-old woman with breast cancer who underwent neoadjuvant chemotherapy and surgery, presenting one year later with bone lesions and anemia. Initially suspected to be metastatic bone disease, the findings of elevated serum total protein and globulins prompted serum protein electrophoresis, which revealed a distinct M band in the gamma region, suggesting a plasma cell neoplasm. Further evaluation confirmed this diagnosis, and treatment was initiated. This case underscores the importance of considering elevated total protein, globulins, and an altered albumin-to-globulin ratio as primary bone marrow disorders, such as plasma cell neoplasms, in breast cancer patients with bone lesions.
Printing allowed the scientific revolution. Scientific journals established peer review. AI is driving the next wave of scientific progress. Ethical aspects of AI in publishing are an emerging area of concern. AI tools are used in generating papers. This raises questions about authorship and accountability: who is responsible? If AI contributes, should they be credited as authors? Are researchers accountable for AI-generated content? If AI is involved in writing, this should be disclosed to maintain transparency. Otherwise, there could be concerns about misrepresentation or lack of rigor. if AI generates portions of a paper, who owns the rights to that work? Frameworks for intellectual property were designed for human creators, so these might be rethought. Many journals require a written statement regarding AI use. AI use in publishing could exacerbate inequality in research access, leading to a divide between well-funded and less-funded institutions. Global inequality in science sharpens: AI might skew research toward countries with more technological resources. relying on AI could undermine the integrity of human oversight. AI does not replace but complements reviewers' expertise. AI-driven tools might lack the nuanced human understanding. Over-reliance on AI could compromise publishing quality. AI offers possibilities to speed up and to improve scientific publishing, but it is essential to judge and to address the ethical implications. This requires guidelines and rules warranting an honest, transparent and integer approach of publishing.
Lead exposure remains a major health concern in the Asia-Pacific, particularly affecting children. Despite its significance, lead toxicity testing is underutilized because of limited awareness, resources, and policy support. On December 16, 2024, the APFCB C-CP (Asia-Pacific Federation for Clinical Biochemistry and Laboratory Medicine -Communication and Publications Committee) conducted Webcast & eLearning Program Webinar themed as "Protecting Health in Asia-Pacific: Laboratory Advances and Lead Exposure Prevention", aimed to address these issues and acknowledge need based solutions. An online survey was conducted during webinar in real time to assess the current lead-testing practices, common exposure sources, testing challenges, and policy changes. A seven-question survey was distributed to webinar participants, covering testing frequency, methodologies, exposure sources, information sources, challenges, and policy needs. A total of 66 professionals attended the session and 22 complete surveys were collected from Nepal, India, Indonesia, Japan, and Australia. Lead testing was infrequent in the region, with 58.6% of the respondents reporting rare or no testing. Weekly testing has been reported in 20.7% of cases. The most commonly used methodology was point-of-care testing via anodic stripping voltammetry (37.5%) followed by electrothermal atomic absorption spectrometry (25%). Occupational exposure (39.1%) was the leading source of lead poisoning, followed by dietary sources (26.1%) and environmental contamination (21.7%). Academic journals (47.5%) were the primary educational resources. Key challenges included low awareness among healthcare providers (43.5%) and resource shortage (39.1%). The most recommended policy change was to increase government support (61.5%). In conclusion, lead testing remains infrequent across many settings, with limited routine implementation and heavy reliance on point-of-care methodologies. Occupational exposure emerged as the predominant source of lead poisoning, underscoring the need for targeted interventions. Strengthening government support is identified as the most critical policy change to enhance lead testing and management efforts.
While Artificial Intelligence (AI) is transforming Laboratory Medicine, successful AI integration depends on the readiness of healthcare professionals. This study aimed to assess the perspectives of Albanian laboratory professionals toward AI integration in medical laboratories. We conducted a cross-sectional, voluntary, and anonymous survey using Google Forms. The survey link was distributed to all members of the Albanian Society of Clinical Biochemistry and Laboratory Medicine and their affiliated staff. The survey explored General Information and Demographics, Digital Properties and Health Data Access, and Perspectives on Artificial Intelligence in Medical Laboratories. Responses were automatically collected over four weeks and were analyzed to investigate laboratory professionals' perspectives. A total of 220 laboratory professionals completed the survey. 30% of participants were laboratory doctors and 70% were laboratory technicians. Participants expressed a generally optimistic outlook on AI integration in medical laboratories, believing it could streamline routine workflows and save time (74%), simplify repetitive tasks (70%), reduce work-related stress (61%), improve analytical accuracy and precision (57%), and reduce costs and enhance efficiency (49%). The main barriers to AI integration were considered high cost of implementation, the lack of appropriate IT infrastructure, the lack of specialized staff, and ethical considerations. Significant differences were observed among various subgroups, but interest in AI training prevailed among the majority of respondents. This survey highlights a generally positive perspective on AI among laboratory professionals in Albania, alongside a strong interest in AI education. According to the survey respondents, strengthening digital infrastructure and promoting training programs will be essential for AI integration in laboratory medicine.
To ensure compliance with new laboratory standards, it is imperative to adopt risk-based thinking, which involves a systematic examination of the functions, procedures, and activities associated with risks and opportunities. This article aims to explore the implementation of risk-based thinking in medical biology laboratories and to highlight the challenges inherent in this approach. This descriptive study was conducted in the biochemistry laboratory of the Main Military Teaching Hospital of Tunis during the first half of 2024. A risk analysis was performed by a working group to identify failures by analyzing non-conformities recorded during the study period. The group adopted the Failure Mode and Effects Analysis (FMEA) methodology, an inductive approach well-suited to process analysis and mastered by all participants. Subsequently, a corrective action plan was developed for each process phase. Across the entire laboratory workflow, 33 distinct failure modes were identified and cataloged for each step, followed by a criticality analysis. The distribution of these failures was 36.36% in the pre-analytical phase, 33.34% in the analytical phase, and 30.3% in the post-analytical phase. A review of the severity of their effects revealed that a significant portion constituted major risks. In response to the major risks identified at each stage of the laboratory workflow, a corrective action plan has been proposed. This plan outlines specific actions designed to reduce the criticality of these risks and enhance patient safety and quality of service.
Synovial fluid analysis plays a crucial role in the diagnosis of periprosthetic joint infection (PJI). However, the stability of leukocyte counts and the percentage of polymorphonuclear neutrophils (PMN%) under different storage conditions remains uncertain, and many institutions lack immediate access to on-site laboratories. We investigated whether storage temperature (room temperature (RT) vs 4 °C) influences synovial fluid white blood cell (WBC) count and PMN%, and if these parameters are stable for up to 72 hours after aspiration. We prospectively analysed 106 synovial fluid samples obtained during revision arthroplasty for suspected PJI. Assuming that the population was homogenous according to the inclusion criteria, patient's samples were randomly allocated either to be stored at RT or at 4°C, in order to obtain two different set of samples. In both set of samples WBC count and PMN% were measured at baseline, 6, 12, 24, 48, and 72 hours using an automated haematology analyser after pre-treatment with hyaluronidase. Changes over time in the same patient synovial fluid and between different storage temperature groups were assessed with independent T test. Both WBC count and PMN% remained stable for up to 72 hours in samples stored at either RT or 4 °C. Mean WBC counts were slightly higher in refrigerated samples, but differences were minimal and not statistically significant. No variation led to reclassification of samples across the ICM 2018 diagnostic thresholds for PJI. Synovial fluid WBC and PMN% remain stable for up to 72 hours regardless of storage temperature. These findings challenge the assumption that immediate analysis is required and support greater flexibility in clinical workflows, particularly in institutions without immediate on-site laboratory availability.
The integration of artificial intelligence (AI) into healthcare and laboratory medicine is reshaping diagnostics, workflows, and patient management. Yet, technological progress alone cannot ensure meaningful outcomes. The concept of Beneficial Intelligence (BI), defined as the synergy of human and artificial intelligence (H + A = B), emphasizes that technology must be guided by human purpose, ethics, and empathy. BI reframes AI not as a replacement for human expertise but as an augmentation that enables laboratory professionals to deliver care that is accurate, sustainable, and patient-centered. In alignment with value-based healthcare, BI prioritizes outcomes that matter most-clinical, operational, economic, and societal. Laboratory medicine provides a fertile ground for this framework, where digitalization, automation, and machine learning models already enhance diagnostics, risk stratification, and decision support. However, responsible adoption requires validation against patient outcomes, adherence to structured evaluation frameworks and continuous human oversight. Ultimately, Beneficial Intelligence is not only a technical model but a mindset: a commitment to ensure that the alliance of human wisdom and AI fosters equitable, efficient, and sustainable healthcare for the future.
Reference intervals (RIs) are critical for accurate clinical decision-making, yet many laboratories rely on manufacturer-provided RIs without local validation. This study assessed the knowledge, attitudes, and practices (KAP) of clinical laboratories in Nepal regarding RI utilization, highlighting challenges and opportunities for standardization in alignment with ISO 15189:2022 accreditation. A nationwide cross-sectional KAP survey was conducted among 56 laboratory professionals. Data were collected via an online questionnaire, covering demographics, RI knowledge, current practices, challenges, and attitudes toward national standardization. Descriptive and inferential statistics (chi-square, Fisher's exact tests) were used for analysis. While 71.4% of respondents correctly defined RIs as the 2.5th-97.5th percentiles, 28.6% held misconceptions. Most laboratories relied on manufacturer-provided RIs (87.5%) or published literature (67.9%). Key challenges to derive one's own RI included method variability and recruiting reference individuals. Accredited labs (ISO 15189) demonstrated better knowledge of RI (93.3% vs. 63.4%, p=0.032) and higher confidence in using current RI (26.7% vs. 7.3%, p=0.047). Strong interest existed in national RI standardization (92.9%) and training (85.7% preferred hands-on workshops). This survey of higher tier clinical laboratories in Nepal reveals that while these laboratories generally understand the importance of reference intervals, significant gaps in practice and standardization remain. The findings highlight an urgent need for inclusive strategies that also address the unique constraints of smaller, widespread laboratories, which perform a large proportion of routine testing in the country. The intense interest in a national program presents an opportunity to improve. Multicenter studies and RI validation integration into accreditation are needed to improve diagnostic accuracy.
HbA1c is a valuable indicator for the diagnosis and therapeutic monitoring of diabetic patients. Our study aims to compare two methods of measuring HbA1c: capillary electrophoresis on the CAPILLARYS 3 OCTA ® (Sebia) with HPLC ADAMS™ (ARKRAY A1c HA-8180T) used routinely in our laboratory, to avoid any discrepancies in patient monitoring in case of changes in the HbA1c measurement method. A total of 103 blood samples from adult patients received at the laboratory have been analyzed in parallel, singly, on both machines. The results show a good correlation between the two systems with a correlation coefficient of 0.991. The Bland and Altman difference diagram shows that the average bias between ADAMS A1c™ and CAPILLARYS 3 OCTA ® is 2.087 mmol/mol (95% CI: 1.7357 to 2.4390 mmol/mol) in IFCC units and 0.19% (95% CI: 0.1602 to 0.2262%) in the National Glycohemoglobin Standardization Program (NGSP) units, and that out of all the patients studied, only four had values outside the limits of the difference diagram. The latter shows a uniform dispersion of values across all the analyzed measurements, with over 95% of the differences between measurements falling within the range [-1.43; 5.61 mmol/mol] or [-0.14; 0.52%]. These results enable us to confirm the reliable transferability between the two techniques without compromising accuracy. Both machines can therefore be used interchangeably or as backup, ensuring homogeneous patient monitoring.
ISO 15189:2022 introduces key updates to medical laboratory standards, emphasizing risk management, ethics, and technical competence. With the December 2025 deadline for ISO 15189:2012 to 15189:2022 transition nearing, a cross-sectional survey was conducted during the Asia-Pacific Federation of Clinical Biochemistry and Laboratory Medicine webinar on February 21, 2025, to assess readiness. On 303 total responses, awareness was high, with 85% familiar with the revised standard and 92% recognizing its stronger focus on risk management. Most (78%) viewed the transition as highly important, and 82% expected improvements in quality and patient care. Major barriers included financial constraints (65%), insufficient training (72%), and resistance to change (45%). Preparation efforts reported were gap analyses (68%), training programs (75%), and policy updates (70%). While optimism is strong, resource limitations and skills gaps threaten timely adoption. The findings highlight the urgent need for structured training, financial support, and expert guidance to help laboratories, particularly in resource-limited settings, meet the new requirements. Collaboration among laboratories, professional bodies, and regulatory authorities will be crucial to ensure a smooth and effective transition to ISO 15189:2022, enabling more accurate, reliable, and patient-centered diagnostics.
Equations traditionally used for estimating low-density lipoprotein cholesterol (LDL-C) have limitations in accuracy and reliability. This study aimed to compare the performance of established equations with a machine learning approach to determine the most appropriate method for LDL-C estimation. A retrospective cross-sectional study was conducted using 14,109 lipid profile records from inpatients and outpatients at Kosumphisai Hospital, Northeastern Thailand (2017-2021). LDL-C was estimated using the Friedewald, Puavilai, National Institutes of Health (NIH), and Martin equations, as well as a Ridge regression model. Direct LDL-C measurement served as the reference standard. Model performance was evaluated using mean absolute error (MAE), the proportion of estimates within ±12% of the direct measurement, and Bland-Altman analysis. The calculation of LDL-C using Ridge regression provided the highest proportion of estimates within the ±12% error margin (75.37%), the lowest MAE (10.05 mg/dL), and the narrowest 95% limits of agreement (-31.19 to 31.57 mg/dL) in Bland-Altman analysis. Ridge regression provided greater accuracy and reliability for LDL-C estimation compared with the four established equations. Future research should consider incorporating additional predictors and alternative penalized regression techniques, such as Lasso or Elastic Net, to enhance model robustness.
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Type 2 diabetes mellitus (T2DM) is a multifactorial disorder where platelet-derived mediators, lipid metabolic pathways, and exocytotic proteins intersect to drive β-cell dysfunction. Activated platelets release serotonin, platelet factor 4 (PF4), sphingosine-1-phosphate (S1P), and microvesicles that trigger oxidative and endoplasmic reticulum (ER) stress in pancreatic islets. CD36-mediated lipid uptake and sphingolipid imbalance intensify ceramide-driven mitochondrial damage. These insults converge on exocytotic failure through disruption of DOC2B, a Ca2+-sensitive mediator of insulin vesicle fusion. Revisiting this axis clarifies how thromboinflammation and lipotoxicity orchestrate β-cell failure and highlights emerging therapeutic targets for T2DM. This review introduces a novel integrative perspective linking platelet-derived mediators, lipid dysregulation, and DOC2B-mediated exocytotic failure as a unified model of β-cell dysfunction in T2DM.