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Laboratory-acquired infections due to a variety of bacteria, viruses, parasites, and fungi have been described over the last century, and laboratory workers are at risk of exposure to these infectious agents. However, reporting laboratory-associated infections has been largely voluntary, and there is no way to determine the real number of people involved or to know the precise risks for workers. In this study, an international survey based on volunteering was conducted in biosafety level 3 and 4 laboratories to determine the number of laboratory-acquired infections and the possible underlying causes of these contaminations. The analysis of the survey reveals that laboratory-acquired infections have been infrequent and even rare in recent years, and human errors represent a very high percentage of the cases. Today, most risks from biological hazards can be reduced through the use of appropriate procedures and techniques, containment devices and facilities, and the training of personnel.

Clinical microbiology laboratories are essential for diagnosing and monitoring antimicrobial resistance (AMR). Here, we assessed the systems involved in generating, managing and analyzing blood culture data in these laboratories in an upper-middle-income country. From October 2023 to February 2024, we conducted a survey on the utilization of automated systems and laboratory information management systems (LIMS) for blood culture specimens in 2022 across 127 clinical microbiology laboratories (one each from 127 public referral hospitals) in Thailand. We categorized automated systems for blood culture processing into three steps: incubation, bacterial identification, and antimicrobial susceptibility testing (AST). Of the 81 laboratories that completed the questionnaires, the median hospital bed count was 450 (range, 150-1,387), and the median number of blood culture bottles processed was 17,351 (range, 2,900-80,330). All laboratories (100%) had an automated blood culture incubation system. Three-quarters of the laboratories (75%, n = 61) had at least one automated system for both bacterial identification and AST, about a quarter (22%, n = 18) had no automated systems for either step, and two laboratories (3%) outsourced both steps. The systems varied and were associated with the hospital level. Many laboratories utilized both automated systems and conventional methods for bacterial identification (n = 54) and AST (n = 61). For daily data management, 71 laboratories (88%) used commercial microbiology LIMS, three (4%) WHONET, three (4%) an in-house database software and four (5%) did not use any software. Many laboratories manually entered data of incubation (73%, n = 59), bacterial identification (27%, n = 22) and AST results (25%, n = 20) from their automated systems into their commercial microbiology LIMS. The most common barrier to data analysis was 'lack of time', followed by 'lack of staff with statistical skills' and 'difficulty in using analytical software'. In Thailand, various automated systems for blood culture and LIMS are utilized. However, barriers to data management and analysis are common. These challenges are likely present in other upper-middle-income countries. We propose that guidance and technical support for automated systems, LIMS and data analysis are needed.

Despite efforts to recruit and retain more women, a stark gender disparity persists within academic science. Abundant research has demonstrated gender bias in many demographic groups, but has yet to experimentally investigate whether science faculty exhibit a bias against female students that could contribute to the gender disparity in academic science. In a randomized double-blind study (n = 127), science faculty from research-intensive universities rated the application materials of a student—who was randomly assigned either a male or female name—for a laboratory manager position. Faculty participants rated the male applicant as significantly more competent and hireable than the (identical) female applicant. These participants also selected a higher starting salary and offered more career mentoring to the male applicant. The gender of the faculty participants did not affect responses, such that female and male faculty were equally likely to exhibit bias against the female student. Mediation analyses indicated that the female student was less likely to be hired because she was viewed as less competent. We also assessed faculty participants’ preexisting subtle bias against women using a standard instrument and found that preexisting subtle bias against women played a moderating role, such that subtle bias against women was associated with less support for the female student, but was unrelated to reactions to the male student. These results suggest that interventions addressing faculty gender bias might advance the goal of increasing the participation of women in science.

Objectives With the worldwide increase of diabetes mellitus prevalence, ensuring the performance of HbA1c assays is essential. Internal quality control (IQC) and external quality assessment (EQA) serve as critical components of quality assurance systems and provide comprehensive performance assessment. We aimed to evaluate the intra‐laboratory and inter‐laboratory variations of HbA1c assays using EQA and IQC data. Methods A total of 326 laboratories continuously participating in the HbA1c EQA program from 2020 to 2023 were included, of which 168 laboratories reported IQC data voluntarily. Acceptance rates and bias were evaluated at three levels: per sample, per year, and per manufacturer. Intra‐laboratory and inter‐laboratory variations were assessed according to biological variation (BV) criteria and clinical guidelines. Results The mean acceptance rates for 20 EQA samples were 48.5%, 77.8%, 86.7% within optimum, desirable, minimum BV criteria. Annual average acceptance rates increased from 91.8% to 96.9% based on EQA criterion. The absolute manufacturer‐specific bias varied from 0.02% to 4.1%. By 2023, the overall inter‐laboratory variation significantly decreased to 2.1%–2.6%. The median intra‐laboratory variations reduced from 1.6% to 1.4% at the low QC level and from 1.2% to 1.0% at the high QC level. 58.9% and 79.8% of laboratories achieved an intra‐laboratory CV < 1.5% for low and high QC levels, respectively. Conclusions Both inter‐laboratory and intra‐laboratory variations of HbA1c measurement have significantly decreased over the years. However, the difference among manufacturers still exists, and ongoing efforts are required to fully comply with clinical guideline requirements.

Most chemical experiments are planned by human scientists and therefore are subject to a variety of human cognitive biases 1 , heuristics 2 and social influences 3 . These anthropogenic chemical reaction data are widely used to train machine-learning models 4 that are used to predict organic 5 and inorganic 6 , 7 syntheses. However, it is known that societal biases are encoded in datasets and are perpetuated in machine-learning models 8 . Here we identify as-yet-unacknowledged anthropogenic biases in both the reagent choices and reaction conditions of chemical reaction datasets using a combination of data mining and experiments. We find that the amine choices in the reported crystal structures of hydrothermal synthesis of amine-templated metal oxides 9 follow a power-law distribution in which 17% of amine reactants occur in 79% of reported compounds, consistent with distributions in social influence models 10 – 12 . An analysis of unpublished historical laboratory notebook records shows similarly biased distributions of reaction condition choices. By performing 548 randomly generated experiments, we demonstrate that the popularity of reactants or the choices of reaction conditions are uncorrelated to the success of the reaction. We show that randomly generated experiments better illustrate the range of parameter choices that are compatible with crystal formation. Machine-learning models that we train on a smaller randomized reaction dataset outperform models trained on larger human-selected reaction datasets, demonstrating the importance of identifying and addressing anthropogenic biases in scientific data. Human scientists make unrepresentative chemical reagent and reaction condition choices, and machine-learning algorithms trained on human-selected experiments are less capable of successfully predicting reaction outcomes than those trained on randomly generated experiments.

[...]many laboratories use a locally developed system of Microsoft Word or similar documents for their policies and perform document control using a combination of spreadsheets and manual updating and version control. Very often this is managed by the laboratory itself, without any significant technical or administrative support from the institution. Because these products have only limited access and version controls, the medical director may not always know if a document has been altered. [...]the commit message becomes the primary means of navigating between versions.

- Inappropriate laboratory test ordering has been shown to be as high as 30%. This can have an important impact on quality of care and costs because of downstream consequences such as additional diagnostics, repeat testing, imaging, prescriptions, surgeries, or hospital stays. - To evaluate the effect of computerized clinical decision support systems on appropriateness of laboratory test ordering. - We used MEDLINE, Embase, CINAHL, MEDLINE In-Process and Other Non-Indexed Citations, Clinicaltrials.gov, Cochrane Library, and Inspec through December 2015. Investigators independently screened articles to identify randomized trials that assessed a computerized clinical decision support system aimed at improving laboratory test ordering by providing patient-specific information, delivered in the form of an on-screen management option, reminder, or suggestion through a computerized physician order entry using a rule-based or algorithm-based system relying on an evidence-based knowledge resource. Investigators extracted data from 30 papers about study design, various study characteristics, study setting, various intervention characteristics, involvement of the software developers in the evaluation of the computerized clinical decision support system, outcome types, and various outcome characteristics. - Because of heterogeneity of systems and settings, pooled estimates of effect could not be made. Data showed that computerized clinical decision support systems had little or no effect on clinical outcomes but some effect on compliance. Computerized clinical decision support systems targeted at laboratory test ordering for multiple conditions appear to be more effective than those targeted at a single condition.

Background & objectives: India has been reporting the cases of coronavirus disease 2019 (COVID-19) since January 30, 2020. The Indian Council of Medical Research (ICMR) formulated and established laboratory surveillance for COVID-19. In this study, an analysis of the surveillance data was done to describe the testing performance and descriptive epidemiology of COVID-19 cases by time, place and person. Methods: The data were extracted from January 22 to April 30, 2020. The frequencies of testing performance were described over time and by place. We described cases by time (epidemic curve by date of specimen collection; seven-day moving average), place (area map) and person (attack rate by age, sex and contact status), and trends were represented along with public health measures and events. Results: Between January 22 and April 30, 2020, a total of 1,021,518 individuals were tested for severe acute respiratory syndrome-coronavirus-2 (SARS-CoV-2). Testing increased from about 250 individuals per day in the beginning of March to 50,000 specimens per day by the end of April 2020. Overall, 40,184 (3.9%) tests were reported positive. The proportion of positive cases was highest among symptomatic and asymptomatic contacts, 2-3-fold higher than among those with severe acute respiratory infection, or those with an international travel history or healthcare workers. The attack rate (per million) by age was highest among those aged 50-69 yr (63.3) and was lowest among those under 10 yr (6.1). The attack rate was higher among males (41.6) than females (24.3). The secondary attack rate was 6.0 per cent. Overall, 99.0 per cent of 736 districts reported testing and 71.1 per cent reported COVID-19 cases. Interpretation & conclusions: The coverage and frequency of ICMR's laboratory surveillance for SARS-CoV-2 improved over time. COVID-19 was reported from most parts of India, and the attack rate was more among men and the elderly and common among close contacts. Analysis of the data indicates that for further insight, additional surveillance tools and strategies at the national and sub-national levels are needed.

Context.—Substantial variability between different antibody titration methods prompted development and introduction of uniform methods in 2008. Objective.—To determine whether uniform methods consistently decrease interlaboratory variation in proficiency testing. Design.—Proficiency testing data for antibody titration between 2009 and 2013 were obtained from the College of American Pathologists. Each laboratory was supplied plasma and red cells to determine anti-A and anti-D antibody titers by their standard method: gel or tube by uniform or other methods at different testing phases (immediate spin and/or room temperature [anti-A], and/or anti-human globulin [AHG: anti-A and anti-D]) with different additives. Interlaboratory variations were compared by analyzing the distribution of titer results by method and phase. Results.—A median of 574 and 1100 responses were reported for anti-A and anti-D antibody titers, respectively, during a 5-year period. The 3 most frequent (median) methods performed for anti-A antibody were uniform tube room temperature (147.5; range, 119–159), uniform tube AHG (143.5; range, 134–150), and other tube AHG (97; range, 82–116); for anti-D antibody, the methods were other tube (451; range, 431–465), uniform tube (404; range, 382–462), and uniform gel (137; range, 121–153). Of the larger reported methods, uniform gel AHG phase for anti-A and anti-D antibodies had the most participants with the same result (mode). For anti-A antibody, 0 of 8 (uniform versus other tube room temperature) and 1 of 8 (uniform versus other tube AHG), and for anti-D antibody, 0 of 8 (uniform versus other tube) and 0 of 8 (uniform versus other gel) proficiency tests showed significant titer variability reduction. Conclusion.—Uniform methods harmonize laboratory techniques but rarely reduce interlaboratory titer variance in comparison with other methods.

Specialists of human genetic diagnostics can be divided into four groups: Medical Geneticists (MDG), Genetic Nurses and/or Counsellors (GN/GC), Clinical Laboratory Geneticists (CLG) and Laboratory Genetics Technicians (LGT). While the first two groups are in direct patient contact, the work of the latter two, of equal importance for patient care, are often hidden as they work behind the scenes. Herein the first study on the rights and duties of CLGs is presented. We present the results of a survey performed in 35 European and 18 non-European countries with 100 participating specialists. A national CLG title is available in 60% of European countries, and in 77% of the surveyed European countries a CLG can be the main responsible head of the laboratory performing human genetic tests. However, in only 20% of European countries is a lab-report valid with only a CLGs’ signature - even though the report is almost always formulated by the CLG, and an interpretation of the obtained results in a clinical context by the CLG is expected in nearly 90% of European countries. Interestingly, CLGs see patients in 30% of European countries, and are also regularly involved in student education. Overall, the CLG profession includes numerous duties, which are quite similar in all regions of the world. Strikingly, the CLG’s rights and responsibilities of leading a lab, or signing a report are regulated differently according to country specific regulations. Overall, the CLG is a well-recognized profession worldwide and often working within a multidisciplinary team of human genetic diagnostics professionals.