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Developing musculoskeletal hand models requires a variety of experimental biomechanics data. However, collecting robust biomechanics hand data is a time intensive process leading to a lack of widely available datasets. To address this issue the biomechanics hand modeling database (BHaM) was made as a collection of experimental data to aid the development, testing, and validation of musculoskeletal models and simulations. BHaM includes two datasets: (1) a population dataset (n = 726 adults) describing hand strength (pinch and grip), self-reported hand function (Michigan Hand Questionnaire), and anthropometric measurements (from photographs), and (2) a biomechanics dataset (n = 30 adults) describing kinematics (marker-based motion capture), kinetics (isometric and isokinetic data), and electromyography (surface and fine wire) during 19 tasks across the elbow, wrist, and hand. A subset of the biomechanics dataset (n = 15 adults) also includes magnetic resonance imaging of the shoulder through wrist. Participants for both datasets were recruited to represent a diverse population of healthy adults, ranging from 18 to 91 years.

Biomechanics studies rely on non-random recruitment methods to obtain study participants. However, the use of common recruitment methods and small sample sizes may influence a given study’s generalizability due to selection bias. To improve generalizability, ecological validity, and participant convenience, recent biomechanics studies have moved beyond lab conditions. However, it is unknown if simply leaving the lab space and increasing sample sizes reduces the risks associated with selection bias. Previous studies relied on chart and literature reviews to identify selection bias. Herein, we build upon this work by exploring the potential for and influence of selection bias in three common recruiting methods by performing an experimental, population-level study on hand biomechanics. Hand biomechanics was assessed in the community using a portable lab setup to measure hand function, grip strength, and pinch strength. A total of 642 apparently healthy participants were recruited across 18 locations, with 426 participants selected based on complete data responses and being between the ages of 18 to 35. Sex stratified analysis was performed to see how recruiting only biomechanists, undergraduate students, or university affiliates changed population estimates of hand strength. The presence of selection bias was observed in all three test cases with both male and female biomechanists, graduate students, and non-university affiliates having significant increases in pinch and grip strengths ranging from 6.2% to 19.4% above overall population values. This study quantitively shows how simply leaving the lab and increasing subject recruitment does not eliminate the potential for selection bias in studies of hand biomechanics.

Acute kidney injury (AKI) is a frequent complication following allogeneic hematopoietic stem cell transplantation (allo-HSCT), but few studies have focused on AKI treated with kidney replacement therapy (AKI-KRT), particularly among critically ill patients. We investigated the incidence, risk factors, and 90-day mortality associated with AKI-KRT in 529 critically ill adult allo-HSCT recipients admitted to the ICU within 1-year post-transplant at two academic medical centers between 2011 and 2021. AKI-KRT occurred in 111 of the 529 patients (21.0%). Lower baseline eGFR, veno-occlusive disease, thrombotic microangiopathy, admission to an ICU within 90 days post-transplant, and receipt of invasive mechanical ventilation (IMV), total bilirubin ≥5.0 mg/dl, and arterial pH <7.40 on ICU admission were each associated with a higher risk of AKI-KRT. Of the 111 patients with AKI-KRT, 97 (87.4%) died within 90 days. Ninety-day mortality was 100% in each of the following subgroups: serum albumin ≤2.0 g/dl, total bilirubin ≥7.0 mg/dl, arterial pH ≤7.20, IMV with moderate-to-severe hypoxemia, and ≥3 vasopressors/inotropes at KRT initiation. AKI-KRT was associated with a 6.59-fold higher adjusted 90-day mortality in critically ill allo-HSCT vs. non-transplanted patients. Short-term mortality remains exceptionally high among critically ill allo-HSCT patients with AKI-KRT, highlighting the importance of multidisciplinary discussions prior to KRT initiation.

Developing musculoskeletal hand models requires a variety of experimental biomechanics data. However, collecting robust biomechanics hand data is a time intensive process leading to a lack of widely available datasets. To address this issue the biomechanics hand modeling database (BHaM) was made as a collection of experimental data to aid the development, testing, and validation of musculoskeletal models and simulations. BHaM includes two datasets: (1) a population dataset (n = 726 adults) describing hand strength (pinch and grip), self-reported hand function (Michigan Hand Questionnaire), and anthropometric measurements (from photographs), and (2) a biomechanics dataset (n = 30 adults) describing kinematics (marker-based motion capture), kinetics (isometric and isokinetic data), and electromyography (surface and fine wire) during 19 tasks across the elbow, wrist, and hand. A subset of the biomechanics dataset (n = 15 adults) also includes magnetic resonance imaging of the shoulder through wrist. Participants for both datasets were recruited to represent a diverse population of healthy adults, ranging from 18 to 91 years.

IntroductionWorldwide, workplace exposure to inorganic dusts and volatile organic compounds (VOCs) have detrimental effect on respiratory health and is a major public health challenge among industrial workers. The characteristic of the respiratory disease outcome is influenced by nature of the inorganic dust or VOCs, dose, duration of exposure and genetic factors. Hence, this study was conducted to evaluate the impact of occupational exposure to varied air pollutants among workers in unorganised and organised industrial sectors.MethodologyThis cross-sectional study was conducted among 150 workers of age within 30–60 years after getting approval from Institutional Ethics Committee and authorities. Subjects with known history of tuberculosis, malignancy, and recent surgery were excluded. Exposure assessment was obtained using a validated questionnaire. The airborne concentration of top 10 VOCs were measured using gas chromatography. Respirable dust and total dust monitoring were carried out with area air samplers as per NIOSH guidelines. Pulmonary function parameters were measured using spirometry.ResultsThis study showed that cumulative VOC exposure index of construction painters was 5.73ppm. Total and respirable dust concentration ranged from 0.1 – 13.7 mg/m3 and 0.07 – 5.2 mg/m3 respectively. The mean Forced Expiratory Volume and peak expiratory flow rate of industry workers were 2.69 ±0.48L and 5.89 ± 1.7L/s respectively. The chief lung function parameters were lower in high exposure (above threshold limit) group than the low exposure group in steel industry.ConclusionThis study demonstrated a decline in the pulmonary function parameters among steel industrial workers than the painters exposed to VOCs. However, the strength of association varied with age, region and duration of exposure. The findings of this study has provided a clear insight about the varied health impacts of VOCs and industrial dusts which would pave way for creating awareness and developing appropriate protective intervention programs.

Quaternary chalcogenides doped with transition metals are currently being investigated due to their low toxicity and abundance in nature. These systems are especially well suited for thin-film solar cells applications due to their abundance in nature. In this study, we used the microwave-assisted solvothermal method to synthesize p-type quaternary semiconducting nanomaterials Cu 2 ZnSnS 4 and Cu 2 Zn 0.5 Ni 0.5 SnS 4 by using one-pot microwave-assisted solvothermal reactions which is treated at 180 – 200 °C (900W power) for 20 min in inside a domestic microwave oven. It was found that a wide range of structural parameters in the synthesized material could be characterized using X-ray diffraction methods, Raman spectral analysis, scanning electron microscopes (FESEM), energy-dispersive spectroscopy (EDS), transmission electron microscope (TEM), and UV–Vis spectrophotometers. It was found that the prepared material exhibited a kesterite crystal structure with crystallite sizes ranging from 20 to 40 nm. FESEM and TEM analysis exposes the morphological features and EDS analysis confirms stoichiometric ratio of the prepared nanomaterials. The optical absorption measurements showed that the nanostructures prepared had band gaps ranging from 1.58 eV to 1.45 eV, according to the optical absorption analysis. In order to finish the thin-film solar cell production process and to further investigate the properties of the CZTS and CZNTS layers, the following additional layers were deposited: CdS with chemical bath deposition; ZnO-Al with RF magnetron deposition; and, finally, Ni-Ag fingers as the front contact. Measurements were made of the thin-film solar cells' efficiency and properties, such as CZTS—3.10% and CZNTS—5.873%, respectively. The prepared materials and properties suggested that these two quaternary chalcogenide systems could be suitable low-cost solar absorber material for thin-film solar cells (TFSC) applications.

The optimized fertilizer usage for better yield of rice cultivation is influenced by key factors like soil fertility, crop variety, duration, season, nutrient content of the fertilizer, time of application etc., It is observed that 60 percent of yield gap in tamilnadu is due to farmers lack of knowledge on key factors and informal sources of information by pesticide dealers. In this study the major contributing factors for fertilizer requirement and optimum crop yield were analyzed based on rough set theory. In data analytics perspective the nutrient plan is sort of multiple attribute decision-making processes. To reduce the complexity of decision making, key factors that are indiscernible to conclusion are eliminated. Our rough set based approach improved the quality of agricultural data through removal of missing and redundant attributes. After pretreatment the data formed as target information, then attribute reduction algorithm was used to derive rules. The generated rules were used to structure the nutrition management decision-making. The precision was above 88% and experiments proved the feasibility of the developed decision support system for nutrient management.