Efficient Removal of Greenhouse Gases: Machine Learning-Assisted Exploration of Metal-Organic Framework Space.

Global warming is a crisis that humanity must face together. With greenhouse gases (GHGs) as the main factor causing global warming, the adoption of relevant processes to eliminate them is essential. With the advantages of high specific surface area, large pore volume, and tunable synthesis, metal-organic frameworks (MOFs) have attracted much attention in GHG storage, adsorption, separation, and catalysis. However, as the pool of MOFs expands rapidly with new syntheses and discoveries, finding a suitable MOF for a particular application is highly challenging. In this regard, high-throughput computational screening is considered the most effective research method for screening a large number of materials to discover high-performance target MOFs. Typically, high-throughput computational screening generates voluminous and multidimensional data, which is well suited for machine learning (ML) training to improve the screening efficiency and explore the relationships between the multidimensional data in depth. This Review summarizes the general process and common methods for using ML to screen MOFs in the field of GHG removal. It also addresses the challenges faced by ML in exploring the MOF space and potential directions for the future development of ML for MOF screening. This aims to enhance the understanding of the integration of ML and MOFs in various fields and broaden the application and development ideas of MOFs.

Tailored Design of Mesoporous Nanospheres with High Entropic Alloy Sites for Efficient Redox Electrocatalysis.

High Entropy Alloys (HEAs) are a versatile material with unique properties, tailored for various applications. They enable pH-sensitive electrocatalytic transformations like hydrogen evolution reaction (HER) and hydrogen oxidation reactions (HOR) in alkaline media. Mesoporous nanostructures with high surface area are preferred for these electrochemical reactions, but designing mesoporous HEA sis challenging. To overcome this challenge, a low-temperature triblock copolymer-assisted wet-chemical approach is developed to produce mesoporous HEA nanospheres composed of PtPdRuMoNi systems with sufficient entropic mixing. Owing to active sites with inherent entropic effect, mesoporous features, and increased accessibility, optimized HEA nanospheres promote strong HER/HOR performance in alkaline medium. At 30 mV nominal overpotential, it exhibits a mass activity of ≈167 (HER) and 151 A gPt -1 (HOR), far exceeding commercial Pt-C electrocatalysts (34 and 48 A gPt -1) and many recently reported various alloys. The Mott-Schottky analysis reveals HEA nanospheres inherit high charge carrier density, positive flat band potential, and smaller charge transfer barrier, resulting in better activity and faster kinetics. This micelle-assisted synthetic enable the exploration of the compositional and configurational spaces of HEAs at relatively low temperature, while simultaneously facilitating the introduction of mesoporous nanostructures for a wide range of catalytic applications.

Isolation, anticancer potency, and camptothecin-producing ability of endophytic fungi isolated from Ixora chinensis.

Camptothecin (CPT) is an important alkaloid used for anticancer treatment. It is mainly produced by two endangered and overharvested Camptotheca acuminata and Nothapodytes nimmoniana plants. Endophytic fungi are promising alternative sources for CPT production. In the present study, fungi residing within explants of Ixora chinensis were isolated and their CPT-producing capability of their endophytes was verified via thin-layer chromatography, high-performance liquid chromatography, liquid chromatography/high resolution mass spectrometry, and nuclear magnetic resonance analyses and compared with standards. In addition, MTT and sulforhodamine B assays were selected to test the anticancer effect. The endophytic fungi collection of 62 isolates were assigned to 11 genera, with four common genera (Diaporthe, Phyllosticta, Colletotrichum, and Phomopsis) and seven less common genera (Penicillium, Botryosphaeria, Fusarium, Pestalotiopsis, Aspergillus, and Didymella). Moreover, the anticancer activity of extracts was assessed against human lung carcinoma (A549). Among eight potential extracts, only Penicillium sp. I3R2 was found to be a source of CPT, while the remaining seven extracts have not been discovered potential secondary compounds. Thus, other prominent endophytic fungi might be potential candidates of phytochemicals with anticancer properties.

First-principles studies of enhanced oxygen reduction reactions on graphene- and nitrogen-doped graphene-coated platinum surfaces.

Developing innovative platinum-based electrocatalysts and enhancing their efficiency are crucial for advancing high-performance fuel cell technology. In this study, we employed DFT calculations to provide a theoretical basis for interpreting the impact of graphene coatings on various Pt surfaces on oxygen reduction reaction (ORR) catalytic activity, which are currently applied as protective layers in experiments. We comprehensively assess the geometric and electronic properties of Pt(100), Pt(110), and Pt(111) surfaces in comparison to their graphene-coated counterparts, revealing different adsorption behaviors of O2 across these surfaces. The ORR mechanisms on different Pt surfaces show distinct rate-determining steps, with Pt(111) showing the highest ORR activity, followed by Pt(110) and Pt(100). Graphene coatings play a key role in enhancing charge transfer from the surface, resulting in modifications of O2 adsorption. Despite influencing ORR kinetics, these graphene-coated surfaces demonstrate competitive catalytic activity compared to their bare counterparts. Notably, Pt(111) with a graphene coating exhibits the lowest activation energy among graphene-coated surfaces. Our calculations also suggest that the ORR can occur directly on non-defective Pt@graphene surfaces rather than being restricted to exposed Pt centers due to point defects on graphene. Furthermore, our work highlights the potential of nitrogen doping onto the Pt(111)@C surface to further enhance ORR activity. This finding positions nitrogen-doped Pt@C as a promising electrocatalyst for advancing electrochemical technologies.

Predictive Value of LACE Scores for Pediatric Readmissions.

INTRODUCTION: Hospital readmissions are recognized as a prevalent, yet potentially preventable, personal and economic burden. Length of stay, Acuity of admission, Comorbidities, and number of Emergency Department visits in the preceding 6 months can be quantified into one score, the LACE score. LACE scores have previously been identified to correlate with hospital readmissions within 30 days of discharge, but research specific to the pediatric population is scant. The objective of the present study was to investigate if LACE scores, in addition to other factors, can be utilized to create a predictive pediatric hospital readmission model that may ultimately be used to decrease readmission rates. METHODS: This study included 25,616 hospitalizations of patients under the age of 18 years. Data were extracted from a hospital network electronic medical record. Demographics included LACE scores, age, gender, race/ethnicity, median household income, and medical centers. The primary exposure variable was LACE score. The main outcome measures were readmissions within 7, 14, and 30 days. The area under the curve (AUC) was used to assess the predictive capability of the regression model on patient 30-day admission. RESULTS: LACE scores, age, gender, race/ethnicity, median household income, and medical centers were examined in a multivariable model to assess patient risk of a 30-day readmission. Only age and LACE score were observed to be statistically significant. The AUC for the combined model was 0.69. DISCUSSION: As only age and LACE score were observed to be statistically significant and the AUC for the combined model was 0.69, this model is considered to have poor predictive capability. CONCLUSIONS: In this study, LACE scores, as well the other factors, had a poor predictive capability for pediatric readmissions.

Multilayer immobilizing of denitrifying Bacillus sp. and TiO2-AgNPs on floating expanded clay carrier for co-treatment of nitrite and pathogens in aquaculture.

Nitrite contamination and the spread of pathogens can seriously degrade water quality. To simultaneously control these factors, an innovative approach of fabricating a remediation agent that contained denitrifying bacteria and TiO2-AgNPs co-immobilized on floating expanded clay (EC) was proposed in this study. The EC was fabricated from a mixture of clay and rice husk through pyrolysis at a high temperature of 1200 °C, followed by a rapid cooling step to create a porous structure for the material. TiO2NPs were modified with Ag to shift the absorbance threshold of TiO2-AgNPs into the visible region of 700-800 nm. The experimental results showed that the stirring speed of 250 rpm was suitable for immobilizing TiO2-AgNPs on EC and achieved the highest Ti and Ag content of 639.38 ± 3.04 and 200.51 ± 3.71 ppm, respectively. Coating TiO2-Ag/EC with chitosan (0.5%) significantly reduced the detachment level of immobilized TiO2-AgNPs compared to that of the material with no coating. In particular, this functionalized material inhibited 99.93 ± 0.1% of Vibrio parahaemolyticus pathogen but did not adversely affect the denitrifying bacteria after 2 h of visible light irradiation. Based on the electrostatic bond between oppositely charged polymers, the denitrifying bacteria, Bacillus sp., in alginate solution was successfully immobilized on the chitosan-coated TiO2-Ag/EC with a bacteria density of (76.67 ± 9.43) × 107 CFU g-1, retaining its nitrite removal efficiency at 99.0 ± 0.27% through six treatment cycles. These findings provide solid evidence for further investigating the combination of biodegradation and photodegradation in wastewater treatment.

The Identification of Enteric Fever-Specific Antigens for Population-Based Serosurveillance.

BACKGROUND: Enteric fever, caused by Salmonella enterica serovars Typhi and Paratyphi A, is a major public health problem in low- and middle-income countries. Moderate sensitivity and scalability of current methods likely underestimate enteric fever burden. Determining the serological responses to organism-specific antigens may improve incidence measures. METHODS: Plasma samples were collected from blood culture-confirmed enteric fever patients, blood culture-negative febrile patients over the course of 3 months, and afebrile community controls. A panel of 17 Salmonella Typhi and Paratyphi A antigens was purified and used to determine antigen-specific antibody responses by indirect ELISAs. RESULTS: The antigen-specific longitudinal antibody responses were comparable between enteric fever patients, patients with blood culture-negative febrile controls, and afebrile community controls for most antigens. However, we found that IgG responses against STY1479 (YncE), STY1886 (CdtB), STY1498 (HlyE), and the serovar-specific O2 and O9 antigens were greatly elevated over a 3-month follow up period in S. Typhi/S. Paratyphi A patients compared to controls, suggesting seroconversion. CONCLUSIONS: We identified a set of antigens as good candidates to demonstrate enteric fever exposure. These targets can be used in combination to develop more sensitive and scalable approaches to enteric fever surveillance and generate invaluable epidemiological data for informing vaccine policies. CLINICAL TRIAL REGISTRATION: ISRCTN63006567.

Mental Health Among Healthcare Workers During the COVID-19 Pandemic in Vietnam.

OBJECTIVES: The objective of this study was to characterize mental health issues among Vietnamese healthcare workers (HCWs) and to identify related factors. METHODS: A cross-sectional study was conducted with 990 HCWs in 2021. Their mental health status was measured using the Depression, Anxiety, and Stress Scale. RESULTS: In total, 49.9%, 52.3%, and 29.8% of respondents were found to have depression, anxiety, and stress, respectively. The multivariable linear regression model revealed that factors associated with increased anxiety scores included depression scores (ß, 0.45; 95% confidence interval [CI], 0.39 to 0.51) and stress scores (ß, 0.46; 95% CI, 0.41 to 0.52). Factors associated with increased depression scores included being frontline HCWs (ß, 0.57; 95% CI, 0.10 to 1.10), stress scores (ß, 0.50; 95% CI, 0.45 to 0.56), and anxiety scores (ß, 0.41; 95% CI, 0.36 to 0.47), while working experience was associated with reduced depression scores (ß, -0.08; 95% CI, -0.16 to -0.01). Factors associated with increased stress scores included working experience (ß, 0.08; 95% CI, 0.00 to 0.16), personal protective equipment interference with daily activities (ß, 0.55; 95% CI, 0.07 to 1.00), depression scores (ß, 0.54; 95% CI, 0.48 to 0.59), and anxiety scores (ß, 0.45; 95% CI, 0.39 to 0.50), while age was associated with reduced stress scores (ß, -0.12; 95% CI, -0.20 to -0.05). CONCLUSIONS: Specific interventions are necessary to enhance and promote the mental health of HCWs so they can successfully cope with the circumstances of the pandemic.

Local Oxidation States in {FeNO}6-8 Porphyrins: Insights from DMRG/CASSCF-CASPT2 Calculations.

A first DMRG/CASSCF-CASPT2 study of a series of paradigmatic {FeNO}6, {FeNO}7, and {FeNO}8 heme-nitrosyl complexes has led to substantial new insight as well as uncovered key shortcomings of the DFT approach. By virtue of its balanced treatment of static and dynamic correlation, the calculations have provided some of the most authoritative information available to date on the energetics of low- versus high-spin states of different classes of heme-nitrosyl complexes. Thus, the calculations indicate low doublet-quartet gaps of 1-4 kcal/mol for {FeNO}7 complexes and high singlet-triplet gaps of ≳20 kcal/mol for both {FeNO}6 and {FeNO}8 complexes. In contrast, DFT calculations yield widely divergent spin state gaps as a function of the exchange-correlation functional. DMRG-CASSCF calculations also help calibrate DFT spin densities for {FeNO}7 complexes, pointing to those obtained from classic pure functionals as the most accurate. The general picture appears to be that nearly all the spin density of Fe[P](NO) is localized on the Fe, while the axial ligand imidazole (ImH) in Fe[P](NO)(ImH) pushes a part of the spin density onto the NO moiety. An analysis of the DMRG-CASSCF wave function in terms of localized orbitals and of the resulting configuration state functions in terms of resonance forms with varying NO(π*) occupancies has allowed us to address the longstanding question of local oxidation states in heme-nitrosyl complexes. The analysis indicates NO(neutral) resonance forms [i.e., Fe(II)-NO0 and Fe(III)-NO0] as the major contributors to both {FeNO}6 and {FeNO}7 complexes. This finding is at variance with the common formulation of {FeNO}6 hemes as Fe(II)-NO+ species but is consonant with an Fe L-edge XAS analysis by Solomon and co-workers. For the {FeNO}8 complex {Fe[P](NO)}-, our analysis suggests a resonance hybrid description: Fe(I)-NO0 ↔ Fe(II)-NO-, in agreement with earlier DFT studies. Vibrational analyses of the compounds studied indicate an imperfect but fair correlation between the NO stretching frequency and NO(π*) occupancy, highlighting the usefulness of vibrational data as a preliminary indicator of the NO oxidation state.

Ensemble learning-based approach for automatic classification of termite mushrooms.

Termite mushrooms are edible fungi that provide significant economic, nutritional, and medicinal value. However, identifying these mushroom species based on morphology and traditional knowledge is ineffective due to their short development time and seasonal nature. This study proposes a novel method for classifying termite mushroom species. The method utilizes Gradient Boosting machine learning techniques and sequence encoding on the Internal Transcribed Spacer (ITS) gene dataset to construct a machine learning model for identifying termite mushroom species. The model is trained using ITS sequences obtained from the National Center for Biotechnology Information (NCBI) and the Barcode of Life Data Systems (BOLD). Ensemble learning techniques are applied to classify termite mushroom species. The proposed model achieves good results on the test dataset, with an accuracy of 0.91 and an average AUCROC value of 0.99. To validate the model, eight ITS sequences collected from termite mushroom samples in An Linh commune, Phu Giao district, Binh Duong province, Vietnam were used as the test data. The results show consistent species identification with predictions from the NCBI BLAST software. The results of species identification were consistent with the NCBI BLAST prediction software. This machine-learning model shows promise as an automatic solution for classifying termite mushroom species. It can help researchers better understand the local growth of these termite mushrooms and develop conservation plans for this rare and valuable plant resource.

Insight into Scattering Mechanisms and Transport Properties of AgCuS for Flexible Thermoelectric Applications.

The development of flexible thermoelectric devices requires materials possessing ductility and high thermoelectric performance at room temperature. However, only a few existing materials meet both criteria. In this study, the ductile properties, electronic structure, and transport properties of the low-temperature phase α-AgCuS were elucidated using first-principles calculations combined with Boltzmann transport theory. With a layered zigzag structure similar to the well-known ductile semiconductor Ag2S, AgCuS is determined to have good metal-like ductility. Through consideration of various intrinsic scattering mechanisms, we found that electron-polar optical phonon interactions have the most significant impact on the transport behavior of AgCuS. The predominance of this type of interaction is also disclosed by the covalent-ionic bonding nature of the Ag-S and Cu-S bonds. Therefore, weakening this interaction via doping or alloying could optimize the thermoelectric performance of the system. At room temperature, a maximum dimensionless figure of merit ZT of up to 0.592 could be achieved under a tuning of hole concentration to 2 × 1019 cm-3, suggesting that α-AgCuS could be a promising p-type candidate for flexible thermoelectric applications.

Predicting Antibiotic Resistance in ICUs Patients by Applying Machine Learning in Vietnam.

Introduction: Artificial Intelligence (AI) and machine learning (ML) are used extensively in HICs to detect and control antibiotic resistance (AMR) in laboratories and clinical institutions. ML is designed to predict outcome variables using an algorithm to enable "machines" to learn the "rules" from the data. ML is increasingly being applied in intensive care units to identify AMR and to assist empiric antibiotic therapy. This study aimed to evaluate the performance of ML models for predicting AMR bacteria and resistance to antibiotics in two Vietnamese hospitals. Patients and Methods: A cross-sectional study combined with retrospective was conducted from 1st January 2020 to 30th June 2022. Five models were developed to predict antibiotic resistance of bacterial infections of ICU patients. Two datasets were prepared to predict AMR bacteria and antibiotics with ML models. The performance of the prediction models was evaluated by various indicators (sensitivity, specificity, precision, accuracy, F1-score, PRC, AuROC, and NormMCC) to determine the optimal time point for data selection. Python version 3.8 was used for statistical analyses. Results: The accuracy, F1-score, AuROC, and normMMC of LightGBM, XGBoost, and Random Forest models were higher than those of other models in both datasets. In both datasets 1 and 2, accuracy, F1-score, AuROC and normMCC of the XGBoost model were the highest among five models (from 0.890 to 1.000). Only Random Forest models had specificity scores higher than 0.850. High scores of sensitivity, accuracy, precision, F1-score, and normMCC indicated that the models were making accurate predictions for datasets 1 and 2. Conclusion: XGBoost, LightGBM, and Random Forest were the best-performed machine learning models to predict antibiotic resistance of bacterial infections of ICUs patients using the patients' EMRs.

Unraveling the Spin-State Energetics of FeN4 Complexes with Ab Initio Methods.

A systematic analysis was conducted to explore the spin-state energetics of a series of 19 FeN4 complexes. The performance of a large number of multireference methods was assessed, highlighting the significant challenges associated with accurately describing the spin-state energetics of FeN4 complexes. Most multireference methods were found to be susceptible to errors originating from the reference CASSCF wavefunction, leading to an overstabilization of high-spin states. Nonetheless, a few multireference methods, namely, CASPT2/CC, DSRG-MRPT3, and LDSRG(2), demonstrated promising performance compared to the benchmark CCSD(T) method. Furthermore, our study revealed that FeN4 complexes having a quintet ground state are exceedingly rare. Accordingly, only one specific model (Fe(L2)) and one synthesized complex (Fe(OTBP)) have the quintet ground state among the studied complexes. This scarcity of quintet FeN4 complexes highlights the unique nature of these systems and raises intriguing questions regarding the factors influencing spin states, such as the size of the macrocycle cavity, the introduction of substituents, or the induction of out-of-plane deformation.

Sleep Quality and Poor Sleep-related Factors Among Healthcare Workers During the COVID-19 Pandemic in Vietnam.

OBJECTIVES: The coronavirus disease 2019 (COVID-19) pandemic has increased the workload of healthcare workers (HCWs), impacting their health. This study aimed to assess sleep quality using the Pittsburgh Sleep Quality Index (PSQI) and identify factors associated with poor sleep among HCWs in Vietnam during the COVID-19 pandemic. METHODS: In this cross-sectional study, 1000 frontline HCWs were recruited from various healthcare facilities in Vietnam between October 2021 and November 2021. Data were collected using a 3-part self-administered questionnaire, which covered demographics, sleep quality, and factors related to poor sleep. Poor sleep quality was defined as a total PSQI score of 5 or higher. RESULTS: Participants' mean age was 33.20±6.81 years (range, 20.0-61.0), and 63.0% were women. The median work experience was 8.54±6.30 years. Approximately 6.3% had chronic comorbidities, such as hypertension and diabetes mellitus. About 59.5% were directly responsible for patient care and treatment, while 7.1% worked in tracing and sampling. A total of 73.8% reported poor sleep quality. Multivariate logistic regression revealed significant associations between poor sleep quality and the presence of chronic comorbidities (odds ratio [OR], 2.34; 95% confidence interval [CI], 1.17 to 5.24), being a frontline HCW directly involved in patient care and treatment (OR, 1.59; 95% CI, 1.16 to 2.16), increased working hours (OR, 1.84; 95% CI,1.37 to 2.48), and a higher frequency of encountering critically ill and dying patients (OR, 1.42; 95% CI, 1.03 to 1.95). CONCLUSIONS: The high prevalence of poor sleep among HCWs in Vietnam during the COVID-19 pandemic was similar to that in other countries. Working conditions should be adjusted to improve sleep quality among this population.

Multimodal multitask learning for predicting MCI to AD conversion using stacked polynomial attention network and adaptive exponential decay.

Early identification and treatment of moderate cognitive impairment (MCI) can halt or postpone Alzheimer's disease (AD) and preserve brain function. For prompt diagnosis and AD reversal, precise prediction in the early and late phases of MCI is essential. This research investigates multimodal framework-based multitask learning in the following situations: (1) Differentiating early mild cognitive impairment (eMCI) from late MCI and (2) predicting when an MCI patient would acquire AD. Clinical data and two radiomics features on three brain areas deduced from magnetic resonance imaging were investigated (MRI). We proposed an attention-based module, Stack Polynomial Attention Network (SPAN), to firmly encode clinical and radiomics data input characteristics for successful representation from a small dataset. To improve multimodal data learning, we computed a potent factor using adaptive exponential decay (AED). We used experiments from the Alzheimer's Disease Neuroimaging Initiative (ADNI) cohort study, which included 249 eMCI and 427 lMCI participants at baseline visits. The proposed multimodal strategy yielded the best c-index score in time prediction of MCI to AD conversion (0.85) and the best accuracy in MCI-stage categorization ([Formula: see text]). Moreover, our performance was equivalent to that of contemporary research.

A Fatal Case of Chronic Granulomatous Disease in a Young Man.

Chronic granulomatous disease (CGD) is a rare X-linked or autosomal recessive disorder of early childhood due to defective nicotinamide adenine dinucleotide phosphate (NADPH) oxidase enzyme in leukocytes. It increases susceptibility to infections by catalase-positive bacteria and fungi. We report a case of an 18-year-old man with CGD who presented to the hospital with septic shock due to bacteremia, pneumonia, and osteomyelitis due to multiple rare microorganisms. Despite aggressive management, he did not survive. Increasing awareness about the common infections in this rare disease, their prevention, and lifelong treatment is warranted.

Discontinuation of Car Seat Tolerance Screening and Postdischarge Adverse Outcomes in Infants Born Preterm.

OBJECTIVE: To study the association between discontinuing predischarge car seat tolerance screening (CSTS) with 30-day postdischarge adverse outcomes in infants born preterm. STUDY DESIGN: Retrospective cohort study involving all infants born preterm from 2010 through 2021 who survived to discharge to home in a 14-hospital integrated health care system. The exposure was discontinuation of CSTS. The primary outcome was a composite rate of death, 911 call-triggered transports, or readmissions associated with diagnostic codes of respiratory disorders, apnea, apparent life-threatening event, or brief resolved unexplained events within 30 days of discharge. Outcomes of infants born in the periods of CSTS and after discontinuation were compared. RESULTS: Twelve of 14 hospitals initially utilized CSTS and contributed patients to the CSTS period; 71.4% of neonatal intensive care unit (NICU) patients and 26.9% of non-NICU infants were screened. All hospitals participated in the discontinuation period; 0.1% was screened. Rates of the unadjusted primary outcome were 1.02% in infants in the CSTS period (n = 21 122) and 1.06% after discontinuation (n = 20 142) (P = .76). The aOR (95% CI) was 0.95 (0.75, 1.19). Statistically insignificant differences between periods were observed in components of the primary outcome, gestational age strata, NICU admission status groups, and other secondary analyses. CONCLUSIONS: Discontinuation of CSTS in a large integrated health care network was not associated with a change in 30-day postdischarge adverse outcomes. CSTS's value as a standard predischarge assessment deserves further evaluation.

Allosteric inhibition of Staphylococcus aureus MenD by 1,4-dihydroxy naphthoic acid: a feedback inhibition mechanism of the menaquinone biosynthesis pathway.

Menaquinones (MKs) are electron carriers in bacterial respiratory chains. In Staphylococcus aureus (Sau), MKs are essential for aerobic and anaerobic respiration. As MKs are redox-active, their biosynthesis likely requires tight regulation to prevent disruption of cellular redox balance. We recently found that the Mycobacterium tuberculosis MenD, the first committed enzyme of the MK biosynthesis pathway, is allosterically inhibited by the downstream metabolite 1,4-dihydroxy-2-naphthoic acid (DHNA). To understand if this is a conserved mechanism in phylogenetically distant genera that also use MK, we investigated whether the Sau-MenD is allosterically inhibited by DHNA. Our results show that DHNA binds to and inhibits the SEPHCHC synthase activity of Sau-MenD enzymes. We identified residues in the DHNA binding pocket that are important for catalysis (Arg98, Lys283, Lys309) and inhibition (Arg98, Lys283). Furthermore, we showed that exogenous DHNA inhibits the growth of Sau, an effect that can be rescued by supplementing the growth medium with MK-4. Our results demonstrate that, despite a lack of strict conservation of the DHNA binding pocket between Mtb-MenD and Sau-MenD, feedback inhibition by DHNA is a conserved mechanism in Sau-MenD and hence the Sau MK biosynthesis pathway. These findings may have implications for the development of anti-staphylococcal agents targeting MK biosynthesis. This article is part of the theme issue 'Reactivity and mechanism in chemical and synthetic biology'.

Sleep Quality and Poor Sleep-related Factors Among Healthcare Workers During the COVID-19 Pandemic in Vietnam / 예방의학회지

Objectives@#The coronavirus disease 2019 (COVID-19) pandemic has increased the workload of healthcare workers (HCWs), impacting their health. This study aimed to assess sleep quality using the Pittsburgh Sleep Quality Index (PSQI) and identify factors associated with poor sleep among HCWs in Vietnam during the COVID-19 pandemic. @*Methods@#In this cross-sectional study, 1000 frontline HCWs were recruited from various healthcare facilities in Vietnam between October 2021 and November 2021. Data were collected using a 3-part self-administered questionnaire, which covered demographics, sleep quality, and factors related to poor sleep. Poor sleep quality was defined as a total PSQI score of 5 or higher. @*Results@#Participants’ mean age was 33.20±6.81 years (range, 20.0-61.0), and 63.0% were women. The median work experience was 8.54±6.30 years. Approximately 6.3% had chronic comorbidities, such as hypertension and diabetes mellitus. About 59.5% were directly responsible for patient care and treatment, while 7.1% worked in tracing and sampling. A total of 73.8% reported poor sleep quality. Multivariate logistic regression revealed significant associations between poor sleep quality and the presence of chronic comorbidities (odds ratio [OR], 2.34; 95% confidence interval [CI], 1.17 to 5.24), being a frontline HCW directly involved in patient care and treatment (OR, 1.59; 95% CI, 1.16 to 2.16), increased working hours (OR, 1.84; 95% CI,1.37 to 2.48), and a higher frequency of encountering critically ill and dying patients (OR, 1.42; 95% CI, 1.03 to 1.95). @*Conclusions@#The high prevalence of poor sleep among HCWs in Vietnam during the COVID-19 pandemic was similar to that in other countries. Working conditions should be adjusted to improve sleep quality among this population.

Ag@AgCl nanoparticles grafted on carbon nanofiber: an efficient visible light plasmonic photocatalyst via bandgap reduction.

A novel silver@silver chloride/carbon nanofiber (Ag@AgCl/CNF) hybrid was synthesized by electrospinning, heat treament, and subsequentin situchemical oxidation strategy. The synthesized materials were characterized using x-ray diffraction, Fourier-transform infrared, UV-Vis diffuse reflectance spectroscopy, scanning electron microscopy, and energy dispersive x-ray. The experimental results reveal that the electrospun AgNO3/PAN was carbonized and reduced to Ag/CNF, the Ag/CNF was then partly oxidized to form Ag@AgCl/CNF in which Ag@AgCl nanoparticles (ca. 10-20 nm in diameter) were uniformly bounded to CNFs (ca. 165 nm in diameter). The obtained Ag@AgCl/CNF was employed for Na2S2O8activation under visible light irradiation to treat Rhodamine B (RhB). A remarkable RhB removal of ca. 94.68% was achieved under optimal conditions, and the influence of various parameters on removal efficiency was studied. Quenching experiments revealed that HO•, SO4•-,1O2, and O2•-were major reactive oxygen species, in which O2•-played a pivotal role in RhB degradation. A possible mechanistic route for RhB degradation was proposed.