Emergence and Transmission of the High-Risk ST78 Clone of OXA-48-Producing Enterobacter hormaechei in a Single Hospital in Taiwan.

Carbapenem-resistant Enterobacter cloacae complex is a significant global healthcare threat, particularly carbapenemase-producing Enterobacter hormaechei (CPEH). From January 2017 to January 2021, twenty-two CPEH isolates from a regional teaching hospital in central Taiwan were identified with the carriage of carbapenemase genes blaKPC-2, blaIMP-8, and predominantly blaOXA-48. Over 80% of these CPEH strains clustered into the high-risk ST78 lineage, carrying a blaOXA-48 IncL plasmid (pOXA48-CREH), nearly identical to the endemic plasmid pOXA48-KP in ST11 Klebsiella pneumoniae. This OXA-48-producing ST78 lineage disseminated clonally from 2018 to 2021 and transferred pOXA48-CREH to ST66 and ST90 E. hormaechei. An IMP-8-producing ST78 strain harboring a blaIMP-8-carrying pIncHI2 plasmid appeared in 2018, and by late 2020, a KPC-2-producing ST78 strain was identified after acquiring a novel blaKPC-2-carrying IncFII plasmid. These findings suggest that the high-risk ST78 lineage of E. hormaechei has emerged as the primary driver behind the transmission of CPEH. ST78 has not only acquired various carbapenemase-gene-carrying plasmids but has also facilitated the transfer of pOXA48-CREH to other lineages. Continuous genomic surveillance and targeted interventions are urgently needed to control the spread of emerging CPEH clones in hospital settings.

Unraveling Differences in the Effects of Ammonium/Amine-Based Additives on the Performance and Stability of Inverted Perovskite Solar Cells.

Additive engineering, with its excellent ability to passivate bulk or surface perovskite defects, has become a common strategy to improve the performance and stability of perovskite solar cells (PVSCs). Among the various additives reported so far, ammonium salts are considered an important branch. It is worth noting that although both ammonium-based additives (R-NH3 +) and amine-based additives (R-NH2) are derivatives of ammonia (NH3), the functions of the two can be easily confused due to their structural similarities. Moreover, there is no comprehensive comparative analysis of them in the literature. Here, the differences between phenethylammonium iodide (PEA+) and phenethylamine (PEA) additives are revealed experimentally and theoretically. The results clearly show that PEA outperforms PEA+ in terms of device performance and stability based on the following three factors: i) PEA's defect passivation capability is superior to that of PEA+; ii) PEA has better hydrophobicity to hinder water ingress; and iii) PEA completely improves the stability of PVSCs by enhancing thermal stability and inhibiting iodide migration in perovskite more effectively than PEA+. As a result, the power conversion efficiency (PCE) of the inverted methylammonium triiodide (MAPbI3) device using PEA increases by ≈15% to over 21%. More importantly, this device exhibits greater ability to prevent water invasion, thermal-induce degradation, and inhibit iodide ion migration, resulting in better long-term stability.

When Do Quantum Mechanical Descriptors Help Graph Neural Networks to Predict Chemical Properties?

Deep graph neural networks are extensively utilized to predict chemical reactivity and molecular properties. However, because of the complexity of chemical space, such models often have difficulty extrapolating beyond the chemistry contained in the training set. Augmenting the model with quantum mechanical (QM) descriptors is anticipated to improve its generalizability. However, obtaining QM descriptors often requires CPU-intensive computational chemistry calculations. To identify when QM descriptors help graph neural networks predict chemical properties, we conduct a systematic investigation of the impact of atom, bond, and molecular QM descriptors on the performance of directed message passing neural networks (D-MPNNs) for predicting 16 molecular properties. The analysis surveys computational and experimental targets, as well as classification and regression tasks, and varied data set sizes from several hundred to hundreds of thousands of data points. Our results indicate that QM descriptors are mostly beneficial for D-MPNN performance on small data sets, provided that the descriptors correlate well with the targets and can be readily computed with high accuracy. Otherwise, using QM descriptors can add cost without benefit or even introduce unwanted noise that can degrade model performance. Strategic integration of QM descriptors with D-MPNN unlocks potential for physics-informed, data-efficient modeling with some interpretability that can streamline de novo drug and material designs. To facilitate the use of QM descriptors in machine learning workflows for chemistry, we provide a set of guidelines regarding when and how to best leverage QM descriptors, a high-throughput workflow to compute them, and an enhancement to Chemprop, a widely adopted open-source D-MPNN implementation for chemical property prediction.

ELIXCYTE®, an Allogenic Adipose-Derived Stem Cell Product, Mitigates Osteoarthritis by Reducing Inflammation and Preventing Cartilage Degradation In Vitro.

Adipose-derived stem cells (ADSCs) comprise a promising therapy for osteoarthritis (OA). The therapeutic potential of ELIXCYTE®, an allogeneic human ADSC (hADSC) product, was demonstrated in a phase I/II OA clinical trial. However, the exact mechanism underlying such effects is not clear. Moreover, studies suggest that interleukin-11 (IL-11) has anti-inflammatory, tissue-regenerative, and immune-regulatory functions. Our aim was to unravel the mechanism associated with the therapeutic effects of ELIXCYTE® on OA and its relationship with IL-11. We cocultured ELIXCYTE® with normal human articular chondrocytes (NHACs) in synovial fluid obtained from individuals with OA (OA-SF) to investigate its effect on chondrocyte matrix synthesis and degradation and inflammation by assessing gene expression and cytokine levels. NHACs exposed to OA-SF exhibited increased MMP13 expression. However, coculturing ELIXCYTE® with chondrocytes in OA-SF reduced MMP13 expression in chondrocytes and downregulated PTGS2 and FGF2 expression in ELIXCYTE®. ELIXCYTE® treatment elevated anti-inflammatory cytokine (IL-1RA, IL-10, and IL-13) levels, and the reduction in MMP13 was positively correlated with IL-11 concentrations in OA-SF. These findings indicate that IL-11 in OA-SF might serve as a predictive biomarker for the ELIXCYTE® treatment response in OA, emphasizing the therapeutic potential of ELIXCYTE® to mitigate OA progression and provide insights into its immunomodulatory effects.

Discovery of Potent STT3A/B Inhibitors and Assessment of Their Multipathogen Antiviral Potential and Safety.

In the aftermath of the COVID-19 pandemic, opportunities to modulate biological pathways common to the lifecycles of viruses need to be carefully considered. N-linked glycosylation in humans is mediated exclusively by the oligosaccharyltransferase complex and is frequently hijacked by viruses to facilitate infection. As such, STT3A/B, the catalytic domain of the OST complex, became an intriguing drug target with broad-spectrum antiviral potential. However, due to the critical role N-linked glycosylation plays in a number of fundamental human processes, the toxicological ramifications of STT3A/B inhibition required attention commensurate to that given to antiviral efficacy. Herein, we describe how known STT3A/B inhibitor NGI-1 inspired the discovery of superior tool compounds which were evaluated in in vitro efficacy and translational safety (e.g., CNS, cardiovascular, liver) studies. The described learnings will appeal to those interested in the therapeutic utility of modulating N-linked glycosylation as well as the broader scientific community.

Collaborative Human-Computer Vision Operative Video Analysis Algorithm for Analyzing Surgical Fluency and Surgical Interruptions in Endonasal Endoscopic Pituitary Surgery: Cohort Study.

BACKGROUND: The endonasal endoscopic approach (EEA) is effective for pituitary adenoma resection. However, manual review of operative videos is time-consuming. The application of a computer vision (CV) algorithm could potentially reduce the time required for operative video review and facilitate the training of surgeons to overcome the learning curve of EEA. OBJECTIVE: This study aimed to evaluate the performance of a CV-based video analysis system, based on OpenCV algorithm, to detect surgical interruptions and analyze surgical fluency in EEA. The accuracy of the CV-based video analysis was investigated, and the time required for operative video review using CV-based analysis was compared to that of manual review. METHODS: The dominant color of each frame in the EEA video was determined using OpenCV. We developed an algorithm to identify events of surgical interruption if the alterations in the dominant color pixels reached certain thresholds. The thresholds were determined by training the current algorithm using EEA videos. The accuracy of the CV analysis was determined by manual review, and the time spent was reported. RESULTS: A total of 46 EEA operative videos were analyzed, with 93.6%, 95.1%, and 93.3% accuracies in the training, test 1, and test 2 data sets, respectively. Compared with manual review, CV-based analysis reduced the time required for operative video review by 86% (manual review: 166.8 and CV analysis: 22.6 minutes; P<.001). The application of a human-computer collaborative strategy increased the overall accuracy to 98.5%, with a 74% reduction in the review time (manual review: 166.8 and human-CV collaboration: 43.4 minutes; P<.001). Analysis of the different surgical phases showed that the sellar phase had the lowest frequency (nasal phase: 14.9, sphenoidal phase: 15.9, and sellar phase: 4.9 interruptions/10 minutes; P<.001) and duration (nasal phase: 67.4, sphenoidal phase: 77.9, and sellar phase: 31.1 seconds/10 minutes; P<.001) of surgical interruptions. A comparison of the early and late EEA videos showed that increased surgical experience was associated with a decreased number (early: 4.9 and late: 2.9 interruptions/10 minutes; P=.03) and duration (early: 41.1 and late: 19.8 seconds/10 minutes; P=.02) of surgical interruptions during the sellar phase. CONCLUSIONS: CV-based analysis had a 93% to 98% accuracy in detecting the number, frequency, and duration of surgical interruptions occurring during EEA. Moreover, CV-based analysis reduced the time required to analyze the surgical fluency in EEA videos compared to manual review. The application of CV can facilitate the training of surgeons to overcome the learning curve of endoscopic skull base surgery. TRIAL REGISTRATION: ClinicalTrials.gov NCT06156020; https://clinicaltrials.gov/study/NCT06156020.

Tailoring parameters for QM/MM simulations: accurate modeling of adsorption and catalysis in zirconium-based metal-organic frameworks.

Quantum mechanics/molecular mechanics (QM/MM) simulations offer an efficient way to model reactions occurring in complex environments. This study introduces a specialized set of charge and Lennard-Jones parameters tailored for electrostatically embedded QM/MM calculations, aiming to accurately model both adsorption processes and catalytic reactions in zirconium-based metal-organic frameworks (Zr-MOFs). To validate our approach, we compare adsorption energies derived from QM/MM simulations against experimental results and Monte Carlo simulation outcomes. The developed parameters showcase the ability of QM/MM simulations to represent long-range electrostatic and van der Waals interactions faithfully. This capability is evidenced by the prediction of adsorption energies with a low root mean square error of 1.1 kcal mol-1 across a wide range of adsorbates. The practical applicability of our QM/MM model is further illustrated through the study of glucose isomerization and epimerization reactions catalyzed by two structurally distinct Zr-MOF catalysts, UiO-66 and MOF-808. Our QM/MM calculations closely align with experimental activation energies. Importantly, the parameter set introduced here is compatible with the widely used universal force field (UFF). Moreover, we thoroughly explore how the size of the cluster model and the choice of density functional theory (DFT) methodologies influence the simulation outcomes. This work provides an accurate and computationally efficient framework for modeling complex catalytic reactions within Zr-MOFs, contributing valuable insights into their mechanistic behaviors and facilitating further advancements in this dynamic area of research.

AutoTemplate: enhancing chemical reaction datasets for machine learning applications in organic chemistry.

This paper presents AutoTemplate, an innovative data preprocessing protocol, addressing the crucial need for high-quality chemical reaction datasets in the realm of machine learning applications in organic chemistry. Recent advances in artificial intelligence have expanded the application of machine learning in chemistry, particularly in yield prediction, retrosynthesis, and reaction condition prediction. However, the effectiveness of these models hinges on the integrity of chemical reaction datasets, which are often plagued by inconsistencies like missing reactants, incorrect atom mappings, and outright erroneous reactions. AutoTemplate introduces a two-stage approach to refine these datasets. The first stage involves extracting meaningful reaction transformation rules and formulating generic reaction templates using a simplified SMARTS representation. This simplification broadens the applicability of templates across various chemical reactions. The second stage is template-guided reaction curation, where these templates are systematically applied to validate and correct the reaction data. This process effectively amends missing reactant information, rectifies atom-mapping errors, and eliminates incorrect data entries. A standout feature of AutoTemplate is its capability to concurrently identify and correct false chemical reactions. It operates on the premise that most reactions in datasets are accurate, using these as templates to guide the correction of flawed entries. The protocol demonstrates its efficacy across a range of chemical reactions, significantly enhancing dataset quality. This advancement provides a more robust foundation for developing reliable machine learning models in chemistry, thereby improving the accuracy of forward and retrosynthetic predictions. AutoTemplate marks a significant progression in the preprocessing of chemical reaction datasets, bridging a vital gap and facilitating more precise and efficient machine learning applications in organic synthesis. SCIENTIFIC CONTRIBUTION: The proposed automated preprocessing tool for chemical reaction data aims to identify errors within chemical databases. Specifically, if the errors involve atom mapping or the absence of reactant types, corrections can be systematically applied using reaction templates, ultimately elevating the overall quality of the database.

The role of prior familiarisation and meaningfulness of verbal and visual stimuli on directed forgetting.

ABSTRACTIntentional forgetting of unwanted information is a crucial cognitive function that is often studied with directed forgetting (DF) procedure, whereby cuing some study materials with Forget (F) instruction impairs their memory compared to cuing with Remember (R) instruction. This study investigates how the nature of information (verbal or pictorial), its semantic significance (meaningful or meaningless), and the degree of prior episodic familiarity influence DF. Before the DF phase, stimuli were familiarised by pre-exposing them 0, 2, or 6 times in a prior preview phase. Finally, memory for all items was assessed with old/new recognition test. Experiment 1 employed words, Experiment 2 utilised fractal images, Experiment 3 featured both meaningful and meaningless object images, and Experiment 4 used words and nonwords. Our results indicate that materials that produced better memory performance are not always harder to intentionally forget. Previewed items showed reduced DF compared to non-previewed items regardless of the nature of information, and meaningless stimuli are challenging to intentionally forget regardless of their degrees of familiarisation unless they are meaningless verbal materials. Collectively, the results highlight the importance of joint consideration of the stimulus format, its meaningfulness, and its episodic familiarity in understanding conditions that interact with intentional forgetting.

A new mechanism of thyroid hormone receptor ß agonists ameliorating nonalcoholic steatohepatitis by inhibiting intestinal lipid absorption via remodeling bile acid profiles.

Excessive dietary calories lead to systemic metabolic disorders, disturb hepatic lipid metabolism, and aggravate nonalcoholic steatohepatitis (NASH). Bile acids (BAs) play key roles in regulating nutrition absorption and systemic energy homeostasis. Resmetirom is a selective thyroid hormone receptor ß (THRß) agonist and the first approved drug for NASH treatment. It is well known that the THRß activation could promote intrahepatic lipid catabolism and improve mitochondrial function, however, its effects on intestinal lipid absorption and BA compositions remain unknown. In the present study, the choline-deficient, L-amino acid defined, high-fat diet (CDAHFD) and high-fat diet plus CCl4 (HFD+CCl4)-induced NASH mice were used to evaluate the effects of resmetirom on lipid and BA composition. We showed that resmetirom administration (10 mg·kg-1·d-1, i.g.) significantly altered hepatic lipid composition, especially reduced the C18:2 fatty acyl chain-containing triglyceride (TG) and phosphatidylcholine (PC) in the two NASH mouse models, suggesting that THRß activation inhibited intestinal lipid absorption since C18:2 fatty acid could be obtained only from diet. Targeted analysis of BAs showed that resmetirom treatment markedly reduced the hepatic and intestinal 12-OH to non-12-OH BAs ratio by suppressing cytochrome P450 8B1 (CYP8B1) expression in both NASH mouse models. The direct inhibition by resmetirom on intestinal lipid absorption was further verified by the BODIPY gavage and the oral fat tolerance test. In addition, disturbance of the altered BA profiles by exogenous cholic acid (CA) supplementation abolished the inhibitory effects of resmetirom on intestinal lipid absorption in both normal and CDAHFD-fed mice, suggesting that resmetirom inhibited intestinal lipid absorption by reducing 12-OH BAs content. In conclusion, we discovered a novel mechanism of THRß agonists on NASH treatment by inhibiting intestinal lipid absorption through remodeling BAs composition, which highlights the multiple regulation of THRß activation on lipid metabolism and extends the current knowledge on the action mechanisms of THRß agonists in NASH treatment.

The Impact of Problematic Internet Use on Adolescent Loneliness-Chain Mediation Effects of Social Support and Family Communication.

Purpose: To explore the relationship between adolescents' problematic Internet use and loneliness and the mediating roles of social support and family communication. Methods: A questionnaire survey of 2483 adolescents aged 12-17 years in 148 cities in China was conducted using the Problematic Internet Use Scale, the Collaborative Social Support Scale, the Family Communication Scale, and the Loneliness Scale. The data were statistically analyzed by SPSS 26.0 and validated by AMOS 28.0. Structural equation modelling (SEM) was conducted to test the effects of problematic Internet use on adolescents' loneliness and the mediating effects of perceptions of social support and family communication. Results: There was a significant positive effect of adolescents' problematic Internet use on loneliness (B-0.471, P<0.001), and the mediating effects of perceptual social support (0.003, 0.012) and family communication (0.008, 0.019) were found to play a chain effect between adolescents' problematic Internet use and loneliness, respectively. Use and feelings of loneliness played a chain mediating role (0.002, 0.006). Conclusion: This study identified the effects of adolescent problematic Internet use on loneliness and its mechanism of action, emphasized the importance of social support and family communication, and provided practical insights for improving family parenting styles and preventing and intervening in adolescent loneliness problems.

Enhancing indicator condition-guided HIV testing in Taiwan: a nationwide case-control study from 2009 to 2015.

BACKGROUND: Although indicator condition (IC)-guided HIV testing (IC-HIVT) is effective at facilitating timely HIV diagnosis, research on IC categories and the related HIV risk in Taiwan is limited. To improve the adoption and spread of IC-HIVT in Taiwan, this study compared the IC categories of people living with HIV (PLWH) and non-HIV controls and investigated delays in the diagnosis of HIV infection. METHODS: This nationwide, retrospective, 1:10-matched case-control study analyzed data from the Notifiable Diseases Surveillance System and National Health Insurance Research Database to evaluate 42 ICs for the 5-year period preceding a matched HIV diagnostic date from 2009 to 2015. The ICs were divided into category 1 ICs (AIDS-defining opportunistic illnesses [AOIs]), category 2 ICs (diseases associated with impaired immunity or malignancy but not AOIs), category 3 ICs (ICs associated with sexual behaviors), and category 4 ICs (mononucleosis or mononucleosis-like syndrome). Logistic regression was used to evaluate the HIV risk associated with each IC category (at the overall and annual levels) before the index date. Wilcoxon rank-sum test was performed to assess changes in diagnostic delays following an incident IC category by HIV transmission routes. RESULTS: Fourteen thousand three hundred forty-seven PLWH were matched with 143,470 non-HIV controls. The prevalence results for all ICs and category 1-4 ICs were, respectively, 42.59%, 11.16%, 15.68%, 26.48%, and 0.97% among PLWH and 8.73%, 1.05%, 4.53%, 3.69%, and 0.02% among non-HIV controls (all P < 0.001). Each IC category posed a significantly higher risk of HIV infection overall and annually. The median (interquartile range) potential delay in HIV diagnosis was 15 (7-44), 324.5 (36-947), 234 (13-976), and 74 (33-476) days for category 1-4 ICs, respectively. Except for category 1 for men who have sex with men, these values remained stable across 2009-2015, regardless of the HIV transmission route. CONCLUSIONS: Given the ongoing HIV diagnostic delay, IC-HIVT should be upgraded and adapted to each IC category to enhance early HIV diagnosis.

Relationship between leaf vein traits of three dominant Quercus species and ecological factors in the Qinling Mountains, China.

We investigated the inter- and intra-species differences of leaf vein traits of three dominant Quercus species, Q. wutaishanica, Q. aliena var. acutiserrata, and Q. variabilis of Niubeiling (subtropical humid climate) and Taohuagou (warm temperate semi-humid climate), located in the eastern and western Qinling Mountains. The nine examined leaf vein traits included primary leaf vein width, secondary leaf vein width, mean fine vein width, primary vein density, fine vein density, vein areole diameter, areole density, 3D fine vein surface area, and fine vein volume. We further elucidated the influencing mechanisms and regulatory pathways of biotic and abiotic factors on leaf vein traits. The results showed that species identity had significant effects on eight out of nine leaf vein traits except 3D fine vein surface area, while habitat had significant effects on primary leaf vein width, secondary leaf vein width, vein areole diameter, fine vein density, and areole density. Altitude had significant effects on primary vein density, mean fine vein width, vein areole diameter, fine vein density and areole density. Habitat, tree species identity, and altitude had significantly interactive effects on primary leaf vein density, 3D fine vein surface area, and fine vein volume. There were significant differences in primary leaf vein width, mean fine vein width, areole density, 3D fine vein surface area, fine vein volume, primary vein density of Q. wutaishanica between the two studied habitats, but the differences were only found in secondary leaf vein width and areole density of Q. aliena var. acutiserrata and Q. variabilis. The examined leaf vein traits were influenced both by biotic and abiotic factors, with varying effect sizes. Among the biotic factors, petiole length, leaf length and width ratio had strong effect on leaf vein traits. Among the abiotic factors, climatic and soil factors had high effect size on vein traits, with the former being higher than the latter. Leaf vein traits were affected directly by biotic factors, but indirectly by abiotic factors (soil and climatic factors) via regulating biotic factors (leaf stoichiometry and leaf phenotypic traits).

In vivo SPECT imaging of Tc-99m radiolabeled exosomes from human umbilical-cord derived mesenchymal stem cells in small animals.

Extracellular vesicles derived from human umbilical cord-derived mesenchymal stem cells (UCMSC-EVs) have been postulated to have therapeutic potential for various diseases. However, the biodistribution and pharmacokinetics of these vesicles are still unclear. For a better understanding of the in vivo properties of UCMSC-EVs, in the present study, these vesicles were first radiolabeled with Technetium-99m (99mTc-UCMSC-EVs) and evaluated using in vivo single photon emission computed tomography (SPECT) imaging and biodistribution experiments. SPECT images demonstrated that the liver and spleen tissues mainly took up the 99mTc-UCMSC-EVs. The biodistribution study observed slight uptake in the thyroid and stomach, indicating that 99mTc-UCMSC-EVs was stable at 24 h in vivo. The pharmacokinetic analyses of the blood half-life demonstrated the quick distribution phase (0.85 ± 0.28 min) and elimination phase (25.22 ± 20.76 min) in mice. This study provides a convenient and efficient method for 99mTc-UCMSC-EVs preparation without disturbing their properties. In conclusion, the biodistribution, quick elimination, and suitable stability in vivo of 99mTc-UCMSC-EVs were quantified by the noninvasive imaging and pharmacokinetic analyses, which provides useful information for indication selection, dosage protocol design, and toxicity assessment in future applications.

Enhancing chemical synthesis: a two-stage deep neural network for predicting feasible reaction conditions.

In the field of chemical synthesis planning, the accurate recommendation of reaction conditions is essential for achieving successful outcomes. This work introduces an innovative deep learning approach designed to address the complex task of predicting appropriate reagents, solvents, and reaction temperatures for chemical reactions. Our proposed methodology combines a multi-label classification model with a ranking model to offer tailored reaction condition recommendations based on relevance scores derived from anticipated product yields. To tackle the challenge of limited data for unfavorable reaction contexts, we employed the technique of hard negative sampling to generate reaction conditions that might be mistakenly classified as suitable, forcing the model to refine its decision boundaries, especially in challenging cases. Our developed model excels in proposing conditions where an exact match to the recorded solvents and reagents is found within the top-10 predictions 73% of the time. It also predicts temperatures within ± 20 [Formula: see text] of the recorded temperature in 89% of test cases. Notably, the model demonstrates its capacity to recommend multiple viable reaction conditions, with accuracy varying based on the availability of condition records associated with each reaction. What sets this model apart is its ability to suggest alternative reaction conditions beyond the constraints of the dataset. This underscores its potential to inspire innovative approaches in chemical research, presenting a compelling opportunity for advancing chemical synthesis planning and elevating the field of reaction engineering. Scientific contribution: The combination of multi-label classification and ranking models provides tailored recommendations for reaction conditions based on the reaction yields. A novel approach is presented to address the issue of data scarcity in negative reaction conditions through data augmentation.

Trends and the associated factors of optimal immunological response and virological response in late anti-retroviral therapy initiation HIV cases in Taiwan from 2009 to 2020.

BACKGROUND: Late cART initiation (CD4 count ≤200 cells/µL or AIDS-defining opportunistic illnesses [AOIs] at cART initiation) impedes CD4 count recovery and virologic suppression after cART initiation. However, studies to evaluate trends of and modifiable factors for optimal immunological response (IR) and virological response (VR) in people living with HIV (PLWH) with late cART initiation with the current HIV treatment strategies are limited. METHODS: We retrospectively identified 475 PLWH with late cART initiation in 2009-2020. Patients were grouped based on the presence of IR (CD4 count ≥200 cells/µL) or VR (plasma viral load [PVL] ≤ 50 copies/mL) within 18 months after cART initiation (403 [84.8%] IR(+) and 72 [15.2%] IR(-); 422 [88.8%] VR(+) and 53 [11.2%] VR(-)). We used Joinpoint regression to identify IR (+) and VR(+) proportion changes. RESULTS: From 2009 to 2020, the proportion of IR(+) patients remained unchanged (75% to 90%, P = 0.102), whereas that of VR(+) patients increased significantly (75% to 95%, P = 0.007). No join point was identified for either IR(+) or VR(+), and the annual percentage change was 0.56% (nonsignificant) and 1.35% (significant) for IR(+) and VR(+), respectively. Compared to IR(-) patients, IR(+) patients were more likely to have a higher pre-cART PVL, to start with a first-line INSTI-based regimen, or to start cART within 14 days of HIV diagnosis but were less likely to have chronic kidney disease, composite AOIs, or a lower pre-cART CD4 count. Compared to VR(-) patients, VR(+) patients were more likely to start a single-tablet regimen but were less likely to have a higher pre-cART PVL. CONCLUSIONS: Our study identified several modifiable factors for optimal IR (rapid cART initiation and INSTI-based regimen initiation) and for optimal VR (STR initiation) among late initiators, which may guide early treatment modifications to reduce their AIDS-defining event incidence and mortality.

Transcranial Direct Current Stimulation (tDCS) Ameliorates Stress-Induced Sleep Disruption via Activating Infralimbic-Ventrolateral Preoptic Projections.

Transcranial direct current stimulation (tDCS) is acknowledged for its non-invasive modulation of neuronal activity in psychiatric disorders. However, its application in insomnia research yields varied outcomes depending on different tDCS types and patient conditions. Our primary objective is to elucidate its efficiency and uncover the underlying mechanisms in insomnia treatment. We hypothesized that anodal prefrontal cortex stimulation activates glutamatergic projections from the infralimbic cortex (IL) to the ventrolateral preoptic area (VLPO) to promote sleep. After administering 0.06 mA of electrical currents for 8 min, our results indicate significant non-rapid eye movement (NREM) enhancement in naïve mice within the initial 3 h post-stimulation, persisting up to 16-24 h. In the insomnia group, tDCS enhanced NREM sleep bout numbers during acute stress response and improved NREM and REM sleep duration in subsequent acute insomnia. Sleep quality, assessed through NREM delta powers, remains unaffected. Interference of the IL-VLPO pathway, utilizing designer receptors exclusively activated by designer drugs (DREADDs) with the cre-DIO system, partially blocked tDCS's sleep improvement in stress-induced insomnia. This study elucidated that the activation of the IL-VLPO pathway mediates tDCS's effect on stress-induced insomnia. These findings support the understanding of tDCS effects on sleep disturbances, providing valuable insights for future research and clinical applications in sleep therapy.

High Prevalence of Carbapenem-Resistant Enterobacterales Producing OXA-48 among Carbapenem-Resistant Isolates in a Regional Hospital in Central Taiwan.

In response to the increasing number of carbapenem-resistant Enterobacterales (CRE), we investigated carbapenemase-producing Klebsiella pneumoniae and non-K. pneumoniae epidemiology and genetics. We collected 76 clinical Enterobacterales and 4 stool surveillance Escherichia coli isolates resistant to ertapenem or imipenem. Using polymerase chain reaction (PCR) and DNA sequencing, we assessed carbapenemases, extended-spectrum ß-lactamases, and AmpC ß-lactamases. Molecular typing via pulsed-field gel electrophoresis (PFGE) and conjugation experiments were conducted to examine resistance gene transfer. Among the 80 isolates, 96.2% harbored at least one carbapenemase gene, with blaOXA-48 in 87.5%. KPC-2 and IMP-8 carbapenemases were found in 15.0 and 22.5% of the isolates, respectively, with 27.5% having 2 or more carbapenemase genes. The PFGE analysis revealed the presence of diverse genotypes. PCR-based plasmid replicon typing identified IncA/C as the most prevalent type among K. pneumoniae isolates (26/29), and IncF and IncFIB among E. coli isolates (22/28). Conjugal transfer was successful for plasmids encoding OXA-48, CTX-M-3, CTX-M-14, CMY-2, and other ß-lactamases, except the KPC-2 gene. In conclusion, our study highlights high carbapenemase prevalence in CRE, primarily OXA-48. Multiple carbapenemases within strains were common, and PFGE showed diverse patterns in these carbapenem-resistant isolates.

Integrating Chemical Information into Reinforcement Learning for Enhanced Molecular Geometry Optimization.

Geometry optimization is a crucial step in computational chemistry, and the efficiency of optimization algorithms plays a pivotal role in reducing computational costs. In this study, we introduce a novel reinforcement-learning-based optimizer that surpasses traditional methods in terms of efficiency. What sets our model apart is its ability to incorporate chemical information into the optimization process. By exploring different state representations that integrate gradients, displacements, primitive type labels, and additional chemical information from the SchNet model, our reinforcement learning optimizer achieves exceptional results. It demonstrates an average reduction of about 50% or more in optimization steps compared to the conventional optimization algorithms that we examined when dealing with challenging initial geometries. Moreover, the reinforcement learning optimizer exhibits promising transferability across various levels of theory, emphasizing its versatility and potential for enhancing molecular geometry optimization. This research highlights the significance of leveraging reinforcement learning algorithms to harness chemical knowledge, paving the way for future advancements in computational chemistry.

Comparison of species composition and community characteristics of Quercus forests on south and north slopes of Taibai Mountain, China.

We examined species composition, community characteristics, diversity, and community similarity of five Quercus communities composed of three Quercus species (Q. variabilis, Q. aliena var. acutiserrata, Q. wutaishanica) on the altitudinal gradient on the south and north slopes of Taibai Mountain. The results showed that there was an altitudinal transition pattern from Q. variabilis pure forest to Q. variabilis-Q. aliena var. acutiserrata mixed forest, Q. aliena var. acutiserrata pure forest, Q. aliena var. acutiserrata-Q. wutaishanica mixed forest and Q. wutaishanica pure forest on the south and north slopes of Taibai Mountain. The main companion species of Quercus community on the north slope were Pinus armandii, Castanea seguinii, and Sorbus alnifolia, and were Pinus tabuliformis, C. seguinii, Carpinus cordata, and Q. spinosa on the south slope. Species richness, woody plant density, and Quercus species dominance on the north slope of Taibai Mountain were higher than those on the south slope. α diversity of tree layer in Quercus community on the south and north slopes of Taibai Mountain increased first, then decreased and then increased with altitude. α diversity of tree layer was higher in mixed forests than pure forests. α diversity of shrub layer was higher than that of tree layer and herb layer in Quercus community on south and north slopes. ß diversity fluctuated greatly along the altitudinal gradient on the south and north slopes, indicating that species composition changed greatly with altitude. Results of redundancy analysis showed that mean warmest month temperature, altitude and tree height accounted for 79.0% of the community diversity on the north slope, and that soil water content, tree height, canopy density and mean annual temperature accounted for 79.6% of the community diversity on the south slope. Overall, Quercus dominance was higher on the north slope of Taibai Mountain, and the substitution distribution pattern of Quercus species was clearer than that on the south slope. Environmental factors related to temperature and precipitation jointly affected α diversity of Quercus communities.