Functional genomics and taxonomic insights into heavy metal tolerant novel bacterium Brevibacterium metallidurans sp. nov. NCCP-602T isolated from tannery effluent in Pakistan.

The strain designated NCCP-602T was isolated from tannery effluent, and displayed aerobic, gram-positive, rod-shaped cells that were characterized by oxidase negative, catalase positive, and non-motile features. The most favourable growth conditions were observed at a temperature of 30°C, pH 7.0, and NaCl concentration of 1% (w/v). It tolerated heavy metals at high concentrations of chromium (3600 ppm), copper (3300 ppm), cadmium (3000 ppm), arsenic (1200 ppm) and lead (1500 ppm). The results of phylogenetic analysis, derived from sequences of the 16S rRNA gene, indicated the position of strain NCCP-602T within genus Brevibacterium and showed that it was closely related to Brevibacterium ammoniilyticum JCM 17537T. Strain NCCP-602 T formed a robust branch that was clearly separate from closely related taxa. A comparison of 16S rRNA gene sequence similarity and dDDH values between the closely related type strains and strain NCCP-602T provided additional evidence supporting the classification of strain NCCP-602T as a distinct novel genospecies. The polar lipid profile included diphosphatidylglycerol, glycolipid, phospholipids and amino lipids. MK-7 and MK-8 were found as the respiratory quinones, while anteiso-C15:0, iso-C15:0, iso-C16:0, iso-C17:0, and anteiso-C17:0 were identified as the predominant cellular fatty acids (> 10%). Considering the convergence of phylogenetic, phenotypic, chemotaxonomic, and genotypic traits, it is suggested that strain NCCP-602 T be classified as a distinct species Brevibacterium metallidurans sp. nov. within genus Brevibacterium with type strain NCCP-602T (JCM 18882T = CGMCC1.62055T).

Integrating molecular pathway with genome-wide association data for causality identification in breast cancer.

OBJECTIVE: The study purpose was to explore the causal association between pyruvate metabolism and breast cancer (BC), as well as the molecular role of key metabolic genes, by using bioinformatics and Mendelian randomization (MR) analysis. METHODS: We retrieved and examined diverse datasets from the GEO database to ascertain differentially acting genes (DAGs) in BC via differential expression analysis. Following this, we performed functional and pathway enrichment analyses to ascertain noteworthy molecular functions and metabolic pathways in BC. Employing MR analysis, we established a causal association between pyruvate metabolism and the susceptibility to BC. Additionally, utilizing the DGIdb database, we identified potential targeted medications that act on genes implicated in the pyruvate metabolic pathway and formulated a competing endogenous RNA (ceRNA) regulatory network in BC. RESULTS: We collected the datasets GSE54002, GSE70947, and GSE22820, and identified a total of 1127 DEGs between the BC and NC groups. GO and KEGG enrichment analysis showed that the molecular functions of these DEGs mainly included mitotic nuclear division, extracellular matrix, signaling receptor activator activity, etc. Metabolic pathways were mainly concentrated in PI3K-Akt signaling pathway, Cytokine-cytokine receptor binding and Pyruvate, Tyrosine, Propanoate and Phenylalanine metabolism, etc. In addition, MR analysis demonstrated a causal relationship between pyruvate metabolism and BC risk. Finally, we constructed a regulatory network between pathway genes (ADH1B, ACSS2, ACACB, ADH1A, ALDH2, and ADH1C) and targeted drugs, as well as a ceRNA (lncRNA-miRNA-mRNA) regulatory network for BC, further revealing their interactions. CONCLUSIONS: Our research revealed a causal association between pyruvate metabolism and BC risk, found that ADH1B, ACSS2, ACACB, ADH1A, ALDH2, and ADH1C takes place an important part in the development of BC in the molecular mechanisms related to pyruvate metabolism, and identified some potential targeted small molecule drugs.

Development and validation of an electronic health record-based algorithm for identifying TBI in the VA: A VA Million Veteran Program study.

The purpose of this study was to develop and validate an algorithm for identifying Veterans with a history of traumatic brain injury (TBI) in the Veterans Affairs (VA) electronic health record using VA Million Veteran Program (MVP) data. Manual chart review (n = 200) was first used to establish 'gold standard' diagnosis labels for TBI ('Yes TBI' vs. 'No TBI'). To develop our algorithm, we used PheCAP, a semi-supervised pipeline that relied on the chart review diagnosis labels to train and create a prediction model for TBI. Cross-validation was used to train and evaluate the proposed algorithm, 'TBI-PheCAP.' TBI-PheCAP performance was compared to existing TBI algorithms and phenotyping methods, and the final algorithm was run on all MVP participants (n = 702,740) to assign a predicted probability for TBI and a binary classification status choosing specificity = 90%. The TBI-PheCAP algorithm had an area under the receiver operating characteristic curve of 0.92, sensitivity of 84%, and positive predictive value (PPV) of 98% at specificity = 90%. TBI-PheCAP generally performed better than other classification methods, with equivalent or higher sensitivity and PPV than existing rules-based TBI algorithms and MVP TBI-related survey data. Given its strong classification metrics, the TBI-PheCAP algorithm is recommended for use in future population-based TBI research.

Systematic Mendelian Randomization Exploring Druggable Genes for Hemorrhagic Strokes.

Patients with hemorrhagic stroke have high rates of morbidity and mortality, and drugs for prevention are very limited. Mendelian randomization (MR) analysis can increase the success rate of drug development by providing genetic evidence. Previous MR analyses only analyzed the role of individual drug target genes in hemorrhagic stroke; therefore, we used MR analysis to systematically explore the druggable genes for hemorrhagic stroke. We sequentially performed summary-data-based MR analysis and two-sample MR analysis to assess the associations of all genes within the database with intracranial aneurysm, intracerebral hemorrhage, and their subtypes. Validated genes were further analyzed by colocalization. Only genes that were positive in all three analyses and were druggable were considered desirable genes. We also explored the mediators of genes affecting hemorrhagic stroke incidence. Finally, the associations of druggable genes with other cardiovascular diseases were analyzed to assess potential side effects. We identified 56 genes that significantly affected hemorrhagic stroke incidence. Moreover, TNFSF12, SLC22A4, SPARC, KL, RELT, and ADORA3 were found to be druggable. The inhibition of TNFSF12, SLC22A4, and SPARC can reduce the risk of intracranial aneurysm, subarachnoid hemorrhage, and intracerebral hemorrhage. Gene-induced hypertension may be a potential mechanism by which these genes cause hemorrhagic stroke. We also found that blocking these genes may cause side effects, such as ischemic stroke and its subtypes. Our study revealed that six druggable genes were associated with hemorrhagic stroke, and the inhibition of TNFSF12, SLC22A4, and SPARC had preventive effects against hemorrhagic strokes.

In-situ revealed inhibition of W2C to excessive oxidation of CoOOH for high-efficiency alkaline overall water splitting.

The design of low-cost, efficient, and stable multifunctional basic catalysts to replace the high-cost noble metal catalysts remains a challenge. In this work, we report a dual-component Co-W2C catalytic system which achieves excellent properties of hydrogen evolution reaction (HER, η10 = 63 mV), oxygen evolution reaction (OER, η10 = 259 mV) and overall water splitting (η10 = 1.53 V) by adjusting the interfacial electronic structure of the material. Further density functional theory (DFT) calculations indicate that the efficient electronic modulation at the W2C/Co interface leads to the generation of favorable hydroxyl and hydrogen species energetics on the hybrid surface. The results of the in-situ Raman spectra show that W2C can suppress the excessive oxidation of the active site during the OER process, and the existence of core-shell structure also protects the W2C substrate. The stable and efficient catalytic performance of Co-W2C is attributed to the common advantages of structural and interface manipulation.

Synthesis of 1,2,4,5-tetra-substituted benzenes via copper-catalyzed dimerization of γ,δ-unsaturated ketones.

An efficient cyclization for the synthesis of 1,2,4,5-tetra-substituted benzenes via copper catalyzed dimerization of γ,δ-unsaturated ketones has been described. This one-pot procedure employs the γ,δ-unsaturated ketones as the sole substrate with multiple C-C bond formation. This protocol features broad substrate scope and provides a facile and robust method to construct polysubstituted benzene derivatives under mild conditions.

Synergistic effects of interface and phase engineering on telluride toward alkaline/neutral hydrogen evolution reaction in freshwater/seawater.

The development of efficient, stable, and versatile hydrogen evolution electrocatalysts is of great meaning, but still faces challenging. Interface engineering and phase engineering have been immensely applied in the field of hydrogen evolution reaction (HER) because of their unique physicochemical properties. However, they are typically used separately, which limits their effectiveness. Herein, we propose an interface-engineered CoMo/CoTe electrocatalyst, consisting of an amorphous CoMo (a-CoMo) layer-encapsulated crystalline CoTe array, achieving the profound optimization of catalytic performance. The experimental results and density functional theory calculations show that the d-band center of the catalyst shifts further upward in contrast with its crystalline-crystalline counterpart, optimizing the electronic structure and the intermediate adsorption, thereby reducing the kinetic barrier of HER. The a-CoMo/CoTe with superhydrophilic/superaerophobic features shows excellent catalytic performance in alkaline, neutral, and simulated seawater environments.

Thalassospira aquimaris sp. nov. and Winogradskyella marincola sp. nov. two marine bacteria isolated from an agar-degrading co-culture.

Two novel Gram-stain-negative, aerobic, and non-motile strains, designated FZY0004T and YYF002T, were isolated from an agar-degrading co-culture, which was obtained from seawater of the intertidal zone of Yancheng City, the Yellow Sea of China. Strain FZY0004T optimally grew at 28 °C, pH 7.0, and 2-6% NaCl, while strain YYF002T optimally grew at 28 °C, pH 7.5, and 2-4% NaCl. Strain FZY0004T possessed Q-9 as the major respiratory quinone, and its major fatty acids (> 10%) were summed feature 8 (C18:1 ω7c), C16:0, and summed feature 3 (C16:1 ω7c/C16:1 ω6c). The polar lipids identified in strain FZY0004T were phosphatidylethanolamine (PE), phosphatidylglycerol (PG), and several unidentified phospholipids (PL) and lipids (L). On the other hand, strain YYF002T had MK-6 as the predominant respiratory quinone and its major fatty acids consisted of iso-C15:0, iso-C15:1 G, and iso-C15:0 3-OH. The polar lipids identified in strain YYF002T were aminolipid (AL), PE, and several unidentified lipids. Strain FZY0004T shared 99.5% 16S rRNA gene sequence similarity and 90.1% average nucleotide identity (ANI) with T. povalilytica Zumi 95T, and strain YYF002T shared 99.2% 16S rRNA gene sequence similarity and 88.2% ANI with W. poriferorum JCM 12885T. The genomic DNA G + C contents of strains FZY0004T and YYF002T were 54.5% and 33.5%, respectively. The phylogenetic, phenotypic, and physiological characteristics permitted the distinction of the two strains from their neighbors, and we thus propose the names Thalassospira aquimaris sp. nov. (type strain FZY0004T = JCM 35895T = MCCC 1K08380T) and Winogradskyella marincola sp. nov. (type strain YYF002T = JCM 35950T = MCCC 1K08382T).

The regulation and function of Nrf2 signaling in ferroptosis-activated cancer therapy.

Ferroptosis is an iron-dependent programmed cell death process that involves lipid oxidation via the Fenton reaction to produce lipid peroxides, causing disruption of the lipid bilayer, which is essential for cellular survival. Ferroptosis has been implicated in the occurrence and treatment response of various types of cancer, and targeting ferroptosis has emerged as a promising strategy for cancer therapy. However, cancer cells can escape cellular ferroptosis by activating or remodeling various signaling pathways, including oxidative stress pathways, thereby limiting the efficacy of ferroptosis-activating targeted therapy. The key anti-oxidative transcription factor, nuclear factor E2 related factor 2 (Nrf2 or NFE2L2), plays a dominant role in defense machinery by reprogramming the iron, intermediate, and glutathione peroxidase 4 (GPX4)-related network and the antioxidant system to attenuate ferroptosis. In this review, we summarize the recent advances in the regulation and function of Nrf2 signaling in ferroptosis-activated cancer therapy and explore the prospect of combining Nrf2 inhibitors and ferroptosis inducers as a promising cancer treatment strategy.

Clinical domain knowledge-derived template improves post hoc AI explanations in pneumothorax classification.

OBJECTIVE: Pneumothorax is an acute thoracic disease caused by abnormal air collection between the lungs and chest wall. Recently, artificial intelligence (AI), especially deep learning (DL), has been increasingly employed for automating the diagnostic process of pneumothorax. To address the opaqueness often associated with DL models, explainable artificial intelligence (XAI) methods have been introduced to outline regions related to pneumothorax. However, these explanations sometimes diverge from actual lesion areas, highlighting the need for further improvement. METHOD: We propose a template-guided approach to incorporate the clinical knowledge of pneumothorax into model explanations generated by XAI methods, thereby enhancing the quality of the explanations. Utilizing one lesion delineation created by radiologists, our approach first generates a template that represents potential areas of pneumothorax occurrence. This template is then superimposed on model explanations to filter out extraneous explanations that fall outside the template's boundaries. To validate its efficacy, we carried out a comparative analysis of three XAI methods (Saliency Map, Grad-CAM, and Integrated Gradients) with and without our template guidance when explaining two DL models (VGG-19 and ResNet-50) in two real-world datasets (SIIM-ACR and ChestX-Det). RESULTS: The proposed approach consistently improved baseline XAI methods across twelve benchmark scenarios built on three XAI methods, two DL models, and two datasets. The average incremental percentages, calculated by the performance improvements over the baseline performance, were 97.8% in Intersection over Union (IoU) and 94.1% in Dice Similarity Coefficient (DSC) when comparing model explanations and ground-truth lesion areas. We further visualized baseline and template-guided model explanations on radiographs to showcase the performance of our approach. CONCLUSIONS: In the context of pneumothorax diagnoses, we proposed a template-guided approach for improving model explanations. Our approach not only aligns model explanations more closely with clinical insights but also exhibits extensibility to other thoracic diseases. We anticipate that our template guidance will forge a novel approach to elucidating AI models by integrating clinical domain expertise.

Characterization of Post-COVID-19 Definitions and Clinical Coding Practices: Longitudinal Study.

BACKGROUND: Post-COVID-19 condition (colloquially known as "long COVID-19") characterized as postacute sequelae of SARS-CoV-2 has no universal clinical case definition. Recent efforts have focused on understanding long COVID-19 symptoms, and electronic health record (EHR) data provide a unique resource for understanding this condition. The introduction of the International Classification of Diseases, Tenth Revision (ICD-10) code U09.9 for "Post COVID-19 condition, unspecified" to identify patients with long COVID-19 has provided a method of evaluating this condition in EHRs; however, the accuracy of this code is unclear. OBJECTIVE: This study aimed to characterize the utility and accuracy of the U09.9 code across 3 health care systems-the Veterans Health Administration, the Beth Israel Deaconess Medical Center, and the University of Pittsburgh Medical Center-against patients identified with long COVID-19 via a chart review by operationalizing the World Health Organization (WHO) and Centers for Disease Control and Prevention (CDC) definitions. METHODS: Patients who were COVID-19 positive with either a U07.1 ICD-10 code or positive polymerase chain reaction test within these health care systems were identified for chart review. Among this cohort, we sampled patients based on two approaches: (1) with a U09.9 code and (2) without a U09.9 code but with a new onset long COVID-19-related ICD-10 code, which allows us to assess the sensitivity of the U09.9 code. To operationalize the long COVID-19 definition based on health agency guidelines, symptoms were grouped into a "core" cluster of 11 commonly reported symptoms among patients with long COVID-19 and an extended cluster that captured all other symptoms by disease domain. Patients having ≥2 symptoms persisting for ≥60 days that were new onset after their COVID-19 infection, with ≥1 symptom in the core cluster, were labeled as having long COVID-19 per chart review. The code's performance was compared across 3 health care systems and across different time periods of the pandemic. RESULTS: Overall, 900 patient charts were reviewed across 3 health care systems. The prevalence of long COVID-19 among the cohort with the U09.9 ICD-10 code based on the operationalized WHO definition was between 23.2% and 62.4% across these health care systems. We also evaluated a less stringent version of the WHO definition and the CDC definition and observed an increase in the prevalence of long COVID-19 at all 3 health care systems. CONCLUSIONS: This is one of the first studies to evaluate the U09.9 code against a clinical case definition for long COVID-19, as well as the first to apply this definition to EHR data using a chart review approach on a nationwide cohort across multiple health care systems. This chart review approach can be implemented at other EHR systems to further evaluate the utility and performance of the U09.9 code.

Heterogeneous associations between interleukin-6 receptor variants and phenotypes across ancestries and implications for therapy.

The Phenome-Wide Association Study (PheWAS) is increasingly used to broadly screen for potential treatment effects, e.g., IL6R variant as a proxy for IL6R antagonists. This approach offers an opportunity to address the limited power in clinical trials to study differential treatment effects across patient subgroups. However, limited methods exist to efficiently test for differences across subgroups in the thousands of multiple comparisons generated as part of a PheWAS. In this study, we developed an approach that maximizes the power to test for heterogeneous genotype-phenotype associations and applied this approach to an IL6R PheWAS among individuals of African (AFR) and European (EUR) ancestries. We identified 29 traits with differences in IL6R variant-phenotype associations, including a lower risk of type 2 diabetes in AFR (OR 0.96) vs EUR (OR 1.0, p-value for heterogeneity = 8.5 × 10-3), and higher white blood cell count (p-value for heterogeneity = 8.5 × 10-131). These data suggest a more salutary effect of IL6R blockade for T2D among individuals of AFR vs EUR ancestry and provide data to inform ongoing clinical trials targeting IL6 for an expanding number of conditions. Moreover, the method to test for heterogeneity of associations can be applied broadly to other large-scale genotype-phenotype screens in diverse populations.

Ruegeria marisflavi sp. nov. and Ruegeria aquimaris sp. nov., isolated from seawater of the Yellow Sea.

Two novel Gram-stain-negative, aerobic, non-motile and rod-shaped bacteria, designated as WL0004T and XHP0148T, were isolated from seawater samples collected from the coastal areas of Nantong and Lianyungang, PR China, respectively. Both strains were found to grow at 10-42 °C (optimum, 37 °C) and with 2.0-5.0 % (w/v) NaCl (optimum, 3.0 %). Strain WL0004T grew at pH 6.0-9.0 (optimum, pH 7.0-8.0), while XHP0148T grew at pH 6.0-10.0 (optimum, pH 7.0-8.0). The major cellular fatty acids (>10 %) of both strains included summed feature 8 (C18 : 1 ω6c and/or C18 : 1 ω7c). In addition, strain WL0004T contained 11-methyl C18 : 1 ω7c and strain XHP0148T contained C12 : 0 3-OH. The respiratory quinone of both strains was ubiquinone-10. The G+C content of genomic DNA of strains WL0004T and XHP0148T were 62.5 and 63.0 mol%, respectively. Strains WL0004T and XHP0148T showed the highest 16S rRNA gene sequence similarity to Ruegeria pomeroyi DSS-3T (99.4 and 99.0 %, respectively), and the 16S rRNA gene-based phylogenetic analysis indicated that the two strains were closely related to members of the genus Ruegeria. The average nucleotide identity and digital DNA-DNA hybridization values among the two strains and type strains of the genus Ruegeria were all below 95 and 70 %, respectively, and the phylogenetic tree reconstructed from the bac120 gene set indicated that the two strains are distinct from each other and the members of the genus Ruegeria. Based on this phenotypic and genotypic characterization, strains WL0004T (=MCCC 1K07523T=JCM 35565T=GDMCC 1.3083T) and XHP0148T (=MCCC 1K07543T=JCM 35569T=GDMCC 1.3089T) should be recognized as representing two novel species of the genus Ruegeria and the names Ruegeria marisflavi sp. nov. and Ruegeria aquimaris sp. nov. are proposed, respectively.

Neurological diagnoses in hospitalized COVID-19 patients associated with adverse outcomes: A multinational cohort study.

Few studies examining the patient outcomes of concurrent neurological manifestations during acute COVID-19 leveraged multinational cohorts of adults and children or distinguished between central and peripheral nervous system (CNS vs. PNS) involvement. Using a federated multinational network in which local clinicians and informatics experts curated the electronic health records data, we evaluated the risk of prolonged hospitalization and mortality in hospitalized COVID-19 patients from 21 healthcare systems across 7 countries. For adults, we used a federated learning approach whereby we ran Cox proportional hazard models locally at each healthcare system and performed a meta-analysis on the aggregated results to estimate the overall risk of adverse outcomes across our geographically diverse populations. For children, we reported descriptive statistics separately due to their low frequency of neurological involvement and poor outcomes. Among the 106,229 hospitalized COVID-19 patients (104,031 patients ≥18 years; 2,198 patients <18 years, January 2020-October 2021), 15,101 (14%) had at least one CNS diagnosis, while 2,788 (3%) had at least one PNS diagnosis. After controlling for demographics and pre-existing conditions, adults with CNS involvement had longer hospital stay (11 versus 6 days) and greater risk of (Hazard Ratio = 1.78) and faster time to death (12 versus 24 days) than patients with no neurological condition (NNC) during acute COVID-19 hospitalization. Adults with PNS involvement also had longer hospital stay but lower risk of mortality than the NNC group. Although children had a low frequency of neurological involvement during COVID-19 hospitalization, a substantially higher proportion of children with CNS involvement died compared to those with NNC (6% vs 1%). Overall, patients with concurrent CNS manifestation during acute COVID-19 hospitalization faced greater risks for adverse clinical outcomes than patients without any neurological diagnosis. Our global informatics framework using a federated approach (versus a centralized data collection approach) has utility for clinical discovery beyond COVID-19.

Stomatohabitans albus gen. nov., sp. nov., an oral living facultative anaerobic actinobacteria isolated form Steller sea lion, and proposal of Stomatohabitantaceae fam. nov. and Stomatohabitantales ord. nov.

Two novel actinobacteria, designated as SYSU M7M538T and SYSU M7M531, were isolated from oral of Eumetopias jubatus in Zhuhai Chimelong Ocean Kingdom, China. The cells of these microorganisms stained Gram-positive and were rod shaped. These strains were facultative anaerobic, and catalase-positive. Optimal growth occurred at 37 °C and pH 7.0 over 7 days of cultivation. Both strains possessed diphosphatidylglycerol, phosphatidylglycerol and phosphocholine as the major polar lipids. The main menaquinone was MK-9(H4). The major fatty acids were C16:0, C17:1w8c, C17:0, C18:1w9c and C18:0. Analyses of genome sequences revealed that the genome size of SYSU M7M538T was 2.1 Mbp with G + C content of 52.5 %, while the genome size of SYSU M7M531 was 2.3 Mbp with G + C content of 52.7 %. The ANI and 16S rRNA gene analysis results showed that the pairwise similarities between the two strains and other recognized Nitriliruptoria species were less than 64.9 % and 89.0 %, respectively. Phylogenetic analysis of the 16S rRNA gene sequences indicated that strains SYSU M7M538T and SYSU M7M531 formed a well-separated phylogenetic branch distinct from other orders of Nitriliruptoria. Based on the data presented here, these two strains are considered to represent a novel species of a novel genus, for which the name Stomatohabitans albus gen. nov., sp. nov., with the type strain SYSU M7M538T (=KCTC 59113T = GDMCC 1.4286T), are proposed. We also propose that these organisms represent a novel family named Stomatohabitantaceae fam. nov. of a novel order Stomatohabitantales ord. nov.

Utility of skin tone on pulse oximetry in critically ill patients: a prospective cohort study.

Importance: Pulse oximetry, a ubiquitous vital sign in modern medicine, has inequitable accuracy that disproportionately affects Black and Hispanic patients, with associated increases in mortality, organ dysfunction, and oxygen therapy. Although the root cause of these clinical performance discrepancies is believed to be skin tone, previous retrospective studies used self-reported race or ethnicity as a surrogate for skin tone. Objective: To determine the utility of objectively measured skin tone in explaining pulse oximetry discrepancies. Design Setting and Participants: Admitted hospital patients at Duke University Hospital were eligible for this prospective cohort study if they had pulse oximetry recorded up to 5 minutes prior to arterial blood gas (ABG) measurements. Skin tone was measured across sixteen body locations using administered visual scales (Fitzpatrick Skin Type, Monk Skin Tone, and Von Luschan), reflectance colorimetry (Delfin SkinColorCatch [L*, individual typology angle {ITA}, Melanin Index {MI}]), and reflectance spectrophotometry (Konica Minolta CM-700D [L*], Variable Spectro 1 [L*]). Main Outcomes and Measures: Mean directional bias, variability of bias, and accuracy root mean square (ARMS), comparing pulse oximetry and ABG measurements. Linear mixed-effects models were fitted to estimate mean directional bias while accounting for clinical confounders. Results: 128 patients (57 Black, 56 White) with 521 ABG-pulse oximetry pairs were recruited, none with hidden hypoxemia. Skin tone data was prospectively collected using 6 measurement methods, generating 8 measurements. The collected skin tone measurements were shown to yield differences among each other and overlap with self-reported racial groups, suggesting that skin tone could potentially provide information beyond self-reported race. Among the eight skin tone measurements in this study, and compared to self-reported race, the Monk Scale had the best relationship with differences in pulse oximetry bias (point estimate: -2.40%; 95% CI: -4.32%, -0.48%; p=0.01) when comparing patients with lighter and dark skin tones. Conclusions and relevance: We found clinical performance differences in pulse oximetry, especially in darker skin tones. Additional studies are needed to determine the relative contributions of skin tone measures and other potential factors on pulse oximetry discrepancies.

Integrative analysis of homologous recombination repair patterns unveils prognostic signatures and immunotherapeutic insights in breast cancer.

Globally, breast cancer (BC) is the leading cause of female death and morbidity. Homologous recombination repair (HRR) is critical in BC. However, the prognostic role and immunotherapy response of HRR in BC remains to be clarified. Firstly, we identified HRR types in BC samples from the Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) dataset (GSE42568) based on 65 HRR genes (HRRGs). A differentially expressed gene (DEG) list for different HRR types was generated. Then, the influences of gene sets composed of these DEGs on biological pathways and BC prognosis were explored. Next, we identified gene clusters based on gene sets composed of DEGs. Genes associated with prognosis for DEGs were identified using univariate Cox regression. Finally, the HRR score was constructed based on genes associated with prognosis. We analyzed how HRR score correlates with tumor mutation burden (TMB), immune cell infiltration (ICI), and immunotherapy response. Three HRR clusters were discovered. HRR subtype A demonstrated decreased infiltration and a high number of immunosuppressive cells with a poor prognosis. DEGs among various HRR types were predominantly enriched in cell cycle and genomic stability-related pathways. The prognostic model based on sixteen DEGs accurately predicted BC prognosis. The HRRGs were differentially expressed in three DEG clusters. TMB, ICI, and immunotherapy responses differed significantly between the high and low HRR groups (HSG, LSG). The HSG was distinguished by a high degree of ICI and low TMB. LSG had a better response to anti-PD-1 or anti-PD-1 and anti-CTLA4 combination therapy. This work revealed that HRR patterns would contribute to predicting prognosis and immunotherapy response in BC, which may benefit patients.

LATTE: Label-efficient incident phenotyping from longitudinal electronic health records.

Electronic health record (EHR) data are increasingly used to support real-world evidence studies but are limited by the lack of precise timings of clinical events. Here, we propose a label-efficient incident phenotyping (LATTE) algorithm to accurately annotate the timing of clinical events from longitudinal EHR data. By leveraging the pre-trained semantic embeddings, LATTE selects predictive features and compresses their information into longitudinal visit embeddings through visit attention learning. LATTE models the sequential dependency between the target event and visit embeddings to derive the timings. To improve label efficiency, LATTE constructs longitudinal silver-standard labels from unlabeled patients to perform semi-supervised training. LATTE is evaluated on the onset of type 2 diabetes, heart failure, and relapses of multiple sclerosis. LATTE consistently achieves substantial improvements over benchmark methods while providing high prediction interpretability. The event timings are shown to help discover risk factors of heart failure among patients with rheumatoid arthritis.

Description of Nesterenkonia aerolata sp. nov., an actinobacterium isolated from air of manufacturing shop in a pharmaceutical factory.

During our studies on the microorganism diversity from air of manufacturing shop in a pharmaceutical factory in Shandong province, China, a Gram-stain-positive, aerobic, cocci-shaped bacterium, designated LY-0111T, was isolated from a settling dish. Strain LY-0111T grew at temperature of 10-42 °C (optimum 35 °C), pH of 5.0-10.0 (optimum pH 7.0) and NaCl concentration of 1-12% (optimum 0.5-3%, w/v). Based on the 16S rRNA gene sequence analysis, the strain shared the highest sequence similarities to Nesterenkonia halophila YIM 70179T (96.2%), and was placed within the radiation of Nesterenkonia species in the phylogenetic trees. The genome of the isolate was sequenced, which comprised 2,931,270 bp with G + C content of 66.5%. A supermatrix tree based on the gene set bac120 indicated that LY-0111T was close related to Nesterenkonia xinjiangensis YIM 70097T (16S rRNA gene sequence similarity 95.3%). Chemotaxonomic analysis indicated that the main respiratory quinones were MK-7, MK-8, and MK-9, the predominant cellular fatty acids were anteiso-C15:0 and iso-C15:0, and the major polar lipids consisted of diphosphatidylglycerol, phosphatidylglycerol and phosphatidylinositol. According to the phenotypic, chemotaxonomic and phylogenetic features, strain LY-0111T is considered to represent a novel species, for which the name Nesterenkonia aerolata sp. nov. is proposed. The type strain is LY-0111T (= JCM 36375T = GDMCC 1.3945T). In addition, Nesterenkonia jeotgali was proposed as a later synonym of Nesterenkonia sandarakina, according to the ANI (96.8%) and dDDH (72.9%) analysis between them.

Trans-Balance: Reducing demographic disparity for prediction models in the presence of class imbalance.

INTRODUCTION: Risk prediction, including early disease detection, prevention, and intervention, is essential to precision medicine. However, systematic bias in risk estimation caused by heterogeneity across different demographic groups can lead to inappropriate or misinformed treatment decisions. In addition, low incidence (class-imbalance) outcomes negatively impact the classification performance of many standard learning algorithms which further exacerbates the racial disparity issues. Therefore, it is crucial to improve the performance of statistical and machine learning models in underrepresented populations in the presence of heavy class imbalance. METHOD: To address demographic disparity in the presence of class imbalance, we develop a novel framework, Trans-Balance, by leveraging recent advances in imbalance learning, transfer learning, and federated learning. We consider a practical setting where data from multiple sites are stored locally under privacy constraints. RESULTS: We show that the proposed Trans-Balance framework improves upon existing approaches by explicitly accounting for heterogeneity across demographic subgroups and cohorts. We demonstrate the feasibility and validity of our methods through numerical experiments and a real application to a multi-cohort study with data from participants of four large, NIH-funded cohorts for stroke risk prediction. CONCLUSION: Our findings indicate that the Trans-Balance approach significantly improves predictive performance, especially in scenarios marked by severe class imbalance and demographic disparity. Given its versatility and effectiveness, Trans-Balance offers a valuable contribution to enhancing risk prediction in biomedical research and related fields.