Short-term cognitive effects of repeated-dose esketamine in adolescents with major depressive disorder and suicidal ideation: a randomized controlled trial.

BACKGROUND: Ketamine and its enantiomer have rapid and robust effects on depressive symptom and suicidal ideation. Little is known about their cognitive effects in adolescents. We aimed to evaluate the short-term effect of esketamine on cognition in adolescents with major depressive disorder (MDD) and suicidal ideation. METHOD: In this randomized-controlled trial, 51 participants aged 13-18 with MDD and suicidal ideation received three intravenous infusions of either esketamine (0.25 mg/kg) or midazolam (0.02 mg/kg). Four dimensions of the MATRICS Consensus Cognitive Battery (MCCB), including processing speed, working memory, verbal learning and visual learning, were assessed at Days 0, 6 and 12. RESULTS: In the linear mixed model, a significant time main effect (F = 12.803, P < 0.001), drug main effect (F = 6.607, P = 0.013), and interaction effect (F = 3.315, P = 0.041) was found in processing speed. Other dimensions including working memory and verbal learning showed significant time main effect (all P < 0.05), but no significant drug or interaction effect (all P > 0.05). Esketamine group showed improvement in processing speed from baseline to Days 6 and 12, and working memory from baseline to Day 12 (all P < 0.05). The generalized estimation equation showed no significant association between baseline cognition and antidepressant or antisuicidal effect (both P > 0.05). CONCLUSIONS: The present study suggested that three-dose subanesthetic esketamine infusions did not harm cognition among adolescents with MDD and suicidal ideation. Instead, esketamine may be associated with improvement in processing speed. TRIAL REGISTRATION: This trial was registered in the Chinese Clinical Trials Registry ( http://www.chictr.org.cn , ChiCTR2000041232).

Enhancing mesenchymal stem cell survival and homing capability to improve cell engraftment efficacy for liver diseases.

Although mesenchymal stem cell (MSC) transplantation provides an alternative strategy for end-stage liver disease (ESLD), further widespread application of MSC therapy is limited owing to low cell engraftment efficiency. Improving cell engraftment efficiency plays a critical role in enhancing MSC therapy for liver diseases. In this review, we summarize the current status and challenges of MSC transplantation for ESLD. We also outline the complicated cell-homing process and highlight how low cell engraftment efficiency is closely related to huge differences in extracellular conditions involved in MSC homing journeys ranging from constant, controlled conditions in vitro to variable and challenging conditions in vivo. Improving cell survival and homing capabilities enhances MSC engraftment efficacy. Therefore, we summarize the current strategies, including hypoxic priming, drug pretreatment, gene modification, and cytokine pretreatment, as well as splenectomy and local irradiation, used to improve MSC survival and homing capability, and enhance cell engraftment and therapeutic efficiency of MSC therapy. We hope that this review will provide new insights into enhancing the efficiency of MSC engraftment in liver diseases.

Combined prediction and design reveals the target recognition mechanism of an intrinsically disordered protein interaction domain.

An increasing number of protein interaction domains and their targets are being found to be intrinsically disordered proteins (IDPs). The corresponding target recognition mechanisms are mostly elusive because of challenges in performing detailed structural analysis of highly dynamic IDP-IDP complexes. Here, we show that by combining recently developed computational approaches with experiments, the structure of the complex between the intrinsically disordered C-terminal domain (CTD) of protein 4.1G and its target IDP region in NuMA can be dissected at high resolution. First, we carry out systematic mutational scanning using dihydrofolate reductase-based protein complementarity analysis to identify essential interaction regions and key residues. The results are found to be highly consistent with an α/ß-type complex structure predicted by AlphaFold2 (AF2). We then design mutants based on the predicted structure using a deep learning protein sequence design method. The solved crystal structure of one mutant presents the same core structure as predicted by AF2. Further computational prediction and experimental assessment indicate that the well-defined core structure is conserved across complexes of 4.1G CTD with other potential targets. Thus, we reveal that an intrinsically disordered protein interaction domain uses an α/ß-type structure module formed through synergistic folding to recognize broad IDP targets. Moreover, we show that computational prediction and experiment can be jointly applied to segregate true IDP regions from the core structural domains of IDP-IDP complexes and to uncover the structure-dependent mechanisms of some otherwise elusive IDP-IDP interactions.

Biomineralization of uranium by Desulfovibrio desulfuricans A3-21ZLL under various hydrochemical conditions.

Uranium pollution in groundwater environment has become an important issue of global concern. In this study, a strain of Desulfovibrio desulfuricans was isolated from the tailings of acid heap leaching, and was shown to be able to remove uranium from water via biosorption, bio-reduction, passive biomineralization under uranium stress, and active metabolically dependent bioaccumulation. This research explored the effects of nutrients, pH, initial uranium and sulfate concentration on the functional groups, uranium valence, and crystal size and morphology of uranium immobilization products. Results showed that tetravalent and hexavalent phosphorus-containing uranium minerals was both formed. In sulfate-containing water where Desulfovibrio desulfuricans A3-21ZLL can grow, the sequestration of uranium by bio-reduction was significantly enhanced compared to that with no sulfate loading or no growth. Ungrown Desulfovibrio desulfuricans A3-21ZLL or dead ones released inorganic phosphate group in response to the stress of uranium, which associated with soluble uranyl ion to form insoluble uranium-containing precipitates. This study revealed the influence of hydrochemical conditions on the mineralogy characteristics and spatial distribution of microbial uranium immobilization products. This study is conducive to the long-term and stable bioremediation of groundwater in decommissioned uranium mining area.

The contribution of polyamine pathway to determinations of diagnosis for treatment-resistant depression: A metabolomic analysis.

OBJECTIVES: Approximately one-third of major depressive disorder (MDD) patients do not respond to standard antidepressants and develop treatment-resistant depression (TRD). We aimed to reveal metabolic differences and discover promising potential biomarkers in TRD. METHODS: Our study recruited 108 participants including healthy controls (n = 40) and patients with TRD (n = 35) and first-episode drug-naive MDD (DN-MDD) (n = 33). Plasma samples were presented to ultra performance liquid chromatography-tandem mass spectrometry. Then, a machine-learning algorithm was conducted to facilitate the selection of potential biomarkers. RESULTS: TRD appeared to be a distinct metabolic disorder from DN-MDD and healthy controls (HCs). Compared to HCs, 199 and 176 differentially expressed metabolites were identified in TRD and DN-MDD, respectively. Of all the metabolites that were identified, spermine, spermidine, and carnosine were considered the most promising biomarkers for diagnosing TRD and DN-MDD patients, with the resulting area under the ROC curve of 0.99, 0.99, and 0.93, respectively. Metabolic pathway analysis yielded compelling evidence of marked changes or imbalances in both polyamine metabolism and energy metabolism, which could potentially represent the primary altered pathways associated with MDD. Additionally, L-glutamine, Beta-alanine, and spermine were correlated with HAMD score. CONCLUSIONS: A more disordered metabolism structure is found in TRD than in DN-MDD and HCs. Future investigations should prioritize the comprehensive analysis of potential roles played by these differential metabolites and disturbances in polyamine pathways in the pathophysiology of TRD and depression.

Applications of Bacterial Cellulose-Based Composite Materials in Hard Tissue Regenerative Medicine.

BACKGROUND: Cartilage, bone, and teeth, as the three primary hard tissues in the human body, have a significant application value in maintaining physical and mental health. Since the development of bacterial cellulose-based composite materials with excellent biomechanical strength and good biocompatibility, bacterial cellulose-based composites have been widely studied in hard tissue regenerative medicine. This paper provides an overview of the advantages of bacterial cellulose-based for hard tissue regeneration and reviews the recent progress in the preparation and research of bacterial cellulose-based composites in maxillofacial cartilage, dentistry, and bone. METHOD: A systematic review was performed by searching the PubMed and Web of Science databases using selected keywords and Medical Subject Headings search terms. RESULTS: Ideal hard tissue regenerative medicine materials should be biocompatible, biodegradable, non-toxic, easy to use, and not burdensome to the human body; In addition, they should have good plasticity and processability and can be prepared into materials of different shapes; In addition, it should have good biological activity, promoting cell proliferation and regeneration. Bacterial cellulose materials have corresponding advantages and disadvantages due to their inherent properties. However, after being combined with other materials (natural/ synthetic materials) to form composite materials, they basically meet the requirements of hard tissue regenerative medicine materials. We believe that it is worth being widely promoted in clinical applications in the future. CONCLUSION: Bacterial cellulose-based composites hold great promise for clinical applications in hard tissue engineering. However, there are still several challenges that need to be addressed. Further research is needed to incorporate multiple disciplines and advance biological tissue engineering techniques. By enhancing the adhesion of materials to osteoblasts, providing cell stress stimulation through materials, and introducing controlled release systems into matrix materials, the practical application of bacterial cellulose-based composites in clinical settings will become more feasible in the near future.

Alterations of functional connectivity in young people with depression mediate the relationship between sleep quality and cognitive function.

BACKGROUND: Sleep disturbances is common in young people with depression, and poor sleep quality affects the ability to learn. In this study, we examined possible resting-state functional connectivity abnormalities between regions of interest, and clarified the relationship with depressive symptoms, sleep quality, and cognitive function. METHODS: Resting-state functional magnetic resonance imaging (fMRI) was collected on 42 healthy controls (HCs), 82 youth depressive patients (44 without sleep disturbances (NSD), and 38 with sleep disturbances (SD)). Regions of interest were defined by using Brainnetome Atlas. Functional connectivity was calculated, and its associations with depressive symptoms, sleep quality, and cognitive function were examined using correlation analysis and mediation analysis. RESULTS: The left and right caudal of cingulate gyrus, tongue and larynx region of postcentral gyrus were significant brain regions in NSD versus SD. The average functional connectivity between these regions was associated with poor sleep quality (r = 0.368, p = 0.001) and worse working memory (r = -0.256, p = 0.023) and mediated the relationship between sleep quality and working memory (c = -0.738, c' = -0.500). LIMITATION: Data consistency in this study was not good enough. This study did not monitor sleep rhythms to provide objective sleep-related data. CONCLUSION: The functional connectivity between the left and right caudal of cingulate gyrus with tongue and larynx region of postcentral gyrus may be the neural mechanism by which sleep disturbances affect working memory. This provides an intervention target for clinically improving cognitive function in young people with depression.

Effects of public-health measures for zeroing out different SARS-CoV-2 variants.

Targeted public health interventions for an emerging epidemic are essential for preventing pandemics. During 2020-2022, China invested significant efforts in strict zero-COVID measures to contain outbreaks of varying scales caused by different SARS-CoV-2 variants. Based on a multi-year empirical dataset containing 131 outbreaks observed in China from April 2020 to May 2022 and simulated scenarios, we ranked the relative intervention effectiveness by their reduction in instantaneous reproduction number. We found that, overall, social distancing measures (38% reduction, 95% prediction interval 31-45%), face masks (30%, 17-42%) and close contact tracing (28%, 24-31%) were most effective. Contact tracing was crucial in containing outbreaks during the initial phases, while social distancing measures became increasingly prominent as the spread persisted. In addition, infections with higher transmissibility and a shorter latent period posed more challenges for these measures. Our findings provide quantitative evidence on the effects of public-health measures for zeroing out emerging contagions in different contexts.

Dynamic QTL mapping revealed primarily the genetic structure of photosynthetic traits in castor (Ricinus communis L.).

High photosynthetic efficiency is the basis of high biomass and high harvest index in castor (Ricinus communis L.). Understanding the genetic law of photosynthetic traits will facilitate the breeding for high photosynthetic efficiency. In this study, the dynamic QTL mapping was performed with the populations F2 and BC1 derived from 2 parents with significant difference in net photosynthetic rate (Pn) at 3 stages, in order to reveal the genetic structure of photosynthetic traits. In F2 population, 26 single-locus QTLs were identified, including 3/3/1 (the QTL number at stage I/II/III, the same below), 1/2/0, 1/2/2, 1/3/1, 0/1/1, and 1/1/2 QTLs conferring Pn, water use efficiency (Wue), transpiration rate (Tr), stomatal conductance (Gs), intercellular CO2 concentration (Ci) and chlorophyll content (Cc), with a phenotypic variation explained (PVE) of 8.40%/8.91%/6.17%, 5.36%/31.74%/0, 7.31%/12.80%/15.15%, 1.60%/6.44%/0.02%, 0/1.10%/0.70% and 2.77%/3.96%/6.50% respectively. And 53 epistatic QTLs (31 pairs) were identified, including 2/2/5, 5/6/3, 4/4/2, 6/3/2, 3/2/0 and 4/0/0 ones conferring the above 6 traits, with a PVE of 6.52%/6.47%/19.04%, 16.72%/15.67%/14.12%, 18.57%/15.58%/7.34%, 21.72%/8.52%/7.13%, 13.33%/4.94%/0 and 7.84%/0/0 respectively. The QTL mapping results in BC1 population were consistent with those in F2 population, except fewer QTLs detected. Most QTLs identified were minor-effect ones, only a few were main-effect ones (PVE > 10%), focused on 2 traits, Wue and Tr, such as qWue1.1, qWue1.2, FqTr1.1, FqTr6, BqWue1.1 and BqTr3; The epistatic effects, especially those related to the dominance effects were the main genetic component of photosynthetic traits, and all the epistatic QTLs had no single-locus effects except qPn1.2, FqGs1.2, FqCi1.2 and qCc3.2; The detected QTLs underlying each trait varied at different stages except stable QTLs qGs1.1, detected at 3 stages, qWue2, qTr1.2 and qCc3.2, detected at 2 stages; 6 co-located QTLs were identified, each of which conferring 2-5 different traits, demonstrated the gene pleiotropy between photosynthetic traits; 2 QTL clusters, located within the marker intervals RCM1842-RCM1335 and RCM523-RCM83, contained 15/5 (F2/BC1) and 4/4 (F2/BC1) QTLs conferring multiple traits, including co-located QTLs and main-effect QTLs. The above results provided new insights into the genetic structure of photosynthetic traits and important references for the high photosynthetic efficiency breeding in castor plant.

Directional and stabilizing selection shaped morphological, reproductive, and physiological traits of the invader Solidago canadensis.

Trait evolution in invasive plant species is important because it can impact demographic parameters key to invasion success. Invasive plant species often show phenotypic clines along geographic and climatic gradients. However, the relative contributions of natural selection and neutral evolutionary processes to phenotypic trait variation among populations of invasive plants remain unclear. A common method to assess whether a trait has been shaped by natural selection or neutral evolutionary processes is to compare the geographical pattern for the trait of interest to the divergence in neutral genetic loci (i.e., Q ST -F ST comparisons). Subsequently, a redundancy analysis (RDA) can facilitate identification of putative agents of natural selection on the trait. Here, we employed both a Q ST -F ST comparisons approach and RDA to infer whether natural selection shaped traits of invasive populations of Solidago canadensis in China and identify the potential environmental drivers of natural selection. We addressed two questions: (1) Did natural selection drive phenotypic trait variation among S. canadensis populations? (2) Did climatic, latitudinal, longitudinal, and altitudinal gradients drive patterns of genetic variation among S. canadensis populations? We found significant directional selection for several morphological and reproductive traits (i.e., Q ST > F ST) and stabilizing selection for physiological traits (i.e., Q ST < F ST). The RDA showed that stem biomass of S. canadensis was strongly positively correlated with longitude, while leaf width ratio and specific leaf area were significantly positively correlated with the mean diurnal range. Stem biomass had a strong negative correlation with annual precipitation. Moreover, height of S. canadensis individuals was strongly positively correlated with altitude and precipitation of the wettest quarter. A longitudinal shift in precipitation seasonality likely selected for larger stem biomass in S. canadensis. Overall, these results suggest that longitudinal and altitudinal clines in climate exerted strong selection pressures that shaped the phenotypic traits of S. canadensis.

Development and validation of a machine learning-based prognostic risk stratification model for acute ischemic stroke.

Acute ischemic stroke (AIS) is a most prevalent cause of serious long-term disability worldwide. Accurate prediction of stroke prognosis is highly valuable for effective intervention and treatment. As such, the present retrospective study aims to provide a reliable machine learning-based model for prognosis prediction in AIS patients. Data from AIS patients were collected retrospectively from the Second Affiliated Hospital of Xuzhou Medical University between August 2017 and July 2019. Independent prognostic factors were identified by univariate and multivariate logistic analysis and used to develop machine learning (ML) models. The ML model performance was assessed by area under the receiver operating characteristic curve (AUC) and radar plot. Shapley Additive explanations (SHAP) values were used to interpret the importance of all features included in the predictive model. A total of 677 AIS patients were included in the present study. Poor prognosis was observed in 209 patients (30.9%). Six variables, including neuron specific enolase (NSE), homocysteine (HCY), S-100ß, dysphagia, C-reactive protein (CRP), and anticoagulation were included to establish ML models. Six different ML algorithms were tested, and Random Forest model was selected as the final predictive model with the greatest AUC of 0.908. Moreover, according to SHAP results, NSE impacted the predictive model the most, followed by HCY, S-100ß, dysphagia, CRP and anticoagulation. Based on the RF model, an online tool was constructed to predict the prognosis of AIS patients and assist clinicians in optimizing patient treatment. The present study revealed that NSE, HCY, CRP, S-100ß, anticoagulation, and dysphagia were important factors for poor prognosis in AIS patients. ML algorithms were used to develop predictive models for predicting the prognosis of AIS patients, with the RF model presenting the optimal performance.

Effect of neoadjuvant therapy on serum transforming growth factor-ß, squamous cell carcinoma associated antigen, and prognosis in patients with locally advanced esophageal cancer.

It was to explore the effect of neoadjuvant therapy (NAT) on serum-related indicators and prognosis of patients with locally advanced esophageal cancer (EC). 400 EC patients were grouped as controls (295 cases, radical EC resection alone) and research group (105 cases, NAT plus radical EC resection). The levels of serum carbohydrate antigen 19-9 (CA19-9), carcinoembryonic antigen (CEA), and cytokeratin 19 fragment antigen 21-1 (CYFRA21-1), programmed death-1 (PD-1), PD-2, transforming growth factor-ß1 (TGF-ß1), and squamous cell carcinoma (SCC) antigen were detected before and after treatment. The follow-up lasted for 3 years. The quality of life (QoL) was evaluated by QLQ-OES24. The recurrence rate, recurrence time, overall survival rate (SR), disease-free SR, and complication rate were compared. Compared with controls, the levels of serum CA19-9, CEA, CYFRA21-1, PD-1, PD-2, TGF-ß1, and SCC were decreased, the QoL score was increased 3 years post-treatment, and the recurrence time was prolonged in the research group (P<0.05). The R0 resection rate, recurrence rate, 3-year overall SR, and disease-free SR of the two groups were 67.12% vs 85.71%, 21.36% vs 6.67%, 56.27% vs 77.14%, 29.83% vs 45.71%, respectively (P<0.05). The complication rates of the two groups were 32.54% and 29.52%, respectively (P>0.05). NAT plus radical resection of EC can effectively reduce the level of serum oncology markers in patients with locally advanced EC, reduce the postoperative recurrence rate, improve QoL and SR, and has high safety.

Crosstalk between Placental Trophoblast and Decidual Immune Cells in Recurrent Miscarriage.

Recurrent miscarriage (RM) is a pregnancy complication associated with dysregulation of the maternal-fetal interface. We aimed to identify dysfunctional interactions between trophoblast cells and decidual immune cells in RM. We downloaded single-cell RNA sequencing (scRNA-seq) datasets (GSE214607) from the Gene Expression Omnibus (GEO) datasets for further analysis using the R software. The data comprised of paired placental and decidual tissues, including those from patients diagnosed with RM and matched healthy controls. A total of 22976 cells were identified in 11 cell types, including trophoblasts, immune cells, and other cells. We divided trophoblast cells into three types and analyzed their interactions with decidual immune cells. Additionally, we re-clustered NK&T cells and macrophages, identified differentially expressed genes (DEGs), enriched their functions, and compared the cell interactions with trophoblast cells in each cell type. Our single-cell atlas of the maternal-fetal interface revealed alterations in the cellular organization of the decidua and placenta, cell type-specific transcriptome, and cell communication between immune and non-immune cells in RM, which are critical for illuminating the pathophysiology of RM.

Molecular mechanisms regulating natural menopause in the female ovary: a study based on transcriptomic data.

Introduction: Natural menopause is an inevitable biological process with significant implications for women's health. However, the molecular mechanisms underlying menopause are not well understood. This study aimed to investigate the molecular and cellular changes occurring in the ovary before and after perimenopause. Methods: Single-cell sequencing data from the GTEx V8 cohort (30-39: 14 individuals; 40-49: 37 individuals; 50-59: 61 individuals) and transcriptome sequencing data from ovarian tissue were analyzed. Seurat was used for single-cell sequencing data analysis, while harmony was employed for data integration. Cell differentiation trajectories were inferred using CytoTrace. CIBERSORTX assessed cell infiltration scores in ovarian tissue. WGCNA evaluated co-expression network characteristics in pre- and post-perimenopausal ovarian tissue. Functional enrichment analysis of co-expression modules was conducted using ClusterprofileR and Metascape. DESeq2 performed differential expression analysis. Master regulator analysis and signaling pathway activity analysis were carried out using MsViper and Progeny, respectively. Machine learning models were constructed using Orange3. Results: We identified the differentiation trajectory of follicular cells in the ovary as ARID5B+ Granulosa -> JUN+ Granulosa -> KRT18+ Granulosa -> MT-CO2+ Granulosa -> GSTA1+ Granulosa -> HMGB1+ Granulosa. Genes driving Granulosa differentiation, including RBP1, TMSB10, SERPINE2, and TMSB4X, were enriched in ATP-dependent activity regulation pathways. Genes involved in maintaining the Granulosa state, such as DCN, ARID5B, EIF1, and HSP90AB1, were enriched in the response to unfolded protein and chaperone-mediated protein complex assembly pathways. Increased contents of terminally differentiated HMGB1+ Granulosa and GSTA1+ Granulosa were observed in the ovaries of individuals aged 50-69. Signaling pathway activity analysis indicated a gradual decrease in TGFb and MAPK pathway activity with menopause progression, while p53 pathway activity increased. Master regulator analysis revealed significant activation of transcription factors FOXR1, OTX2, MYBL2, HNF1A, and FOXN4 in the 30-39 age group, and GLI1, SMAD1, SMAD7, APP, and EGR1 in the 40-49 age group. Additionally, a diagnostic model based on 16 transcription factors (Logistic Regression L2) achieved reliable performance in determining ovarian status before and after perimenopause. Conclusion: This study provides insights into the molecular and cellular mechanisms underlying natural menopause in the ovary. The findings contribute to our understanding of perimenopausal changes and offer a foundation for health management strategies for women during this transition.

S1P-S1PR3-RAS promotes the progression of S1PR3hi TAL1+ T-cell acute lymphoblastic leukemia that can be effectively inhibited by an S1PR3 antagonist.

TAL1+ T-cell acute lymphoblastic leukemia (T-ALL) is a distinct subtype of leukemia with poor outcomes. Through the cooperation of co-activators, including RUNX1, GATA3, and MYB, the TAL1 oncoprotein extends the immature thymocytes with autonomy and plays an important role in the development of T-ALL. However, this process is not yet well understood. Here, by investigating the transcriptome and prognosis of T-ALL from multiple cohorts, we found that S1PR3 was highly expressed in a subset of TAL1+ T-ALL (S1PR3hi TAL1+ T-ALL), which showed poor outcomes. Through pharmacological and genetic methods, we identified a specific survival-supporting role of S1P-S1PR3 in TAL1+ T-ALL cells. In T-ALL cells, TAL1-RUNX1 up-regulated the expression of S1PR3 by binding to the enhancer region of S1PR3 gene. With hyperactivated S1P-S1PR3, T-ALL cells grew rapidly, partly by activating the KRAS signal. Finally, we assessed S1PR3 inhibitor TY-52156 in T-ALL patient-derived xenografts (PDXs) mouse model. We found that TY-52156 attenuated leukemia progression efficiently and extended the lifespan of S1PR3hi TAL1+ T-ALL xenografts. Our findings demonstrate that S1PR3 plays an important oncogenic role in S1PR3hi TAL1+ T-ALL and may serve as a promising therapeutic target.

Masked autoencoder for highly compressed single-pixel imaging.

The single-pixel imaging technique uses multiple patterns to modulate the entire scene and then reconstructs a two-dimensional (2-D) image from the single-pixel measurements. Inspired by the statistical redundancy of natural images that distinct regions of an image contain similar information, we report a highly compressed single-pixel imaging technique with a decreased sampling ratio. This technique superimposes an occluded mask onto modulation patterns, realizing that only the unmasked region of the scene is modulated and acquired. In this way, we can effectively decrease 75% modulation patterns experimentally. To reconstruct the entire image, we designed a highly sparse input and extrapolation network consisting of two modules: the first module reconstructs the unmasked region from one-dimensional (1-D) measurements, and the second module recovers the entire scene image by extrapolation from the neighboring unmasked region. Simulation and experimental results validate that sampling 25% of the region is enough to reconstruct the whole scene. Our technique exhibits significant improvements in peak signal-to-noise ratio (PSNR) of 1.5 dB and structural similarity index measure (SSIM) of 0.2 when compared with conventional methods at the same sampling ratios. The proposed technique can be widely applied in various resource-limited platforms and occluded scene imaging.

Predicting gastric cancer tumor mutational burden from histopathological images using multimodal deep learning.

Tumor mutational burden (TMB) is a significant predictive biomarker for selecting patients that may benefit from immune checkpoint inhibitor therapy. Whole exome sequencing is a common method for measuring TMB; however, its clinical application is limited by the high cost and time-consuming wet-laboratory experiments and bioinformatics analysis. To address this challenge, we downloaded multimodal data of 326 gastric cancer patients from The Cancer Genome Atlas, including histopathological images, clinical data and various molecular data. Using these data, we conducted a comprehensive analysis to investigate the relationship between TMB, clinical factors, gene expression and image features extracted from hematoxylin and eosin images. We further explored the feasibility of predicting TMB levels, i.e. high and low TMB, by utilizing a residual network (Resnet)-based deep learning algorithm for histopathological image analysis. Moreover, we developed a multimodal fusion deep learning model that combines histopathological images with omics data to predict TMB levels. We evaluated the performance of our models against various state-of-the-art methods using different TMB thresholds and obtained promising results. Specifically, our histopathological image analysis model achieved an area under curve (AUC) of 0.749. Notably, the multimodal fusion model significantly outperformed the model that relied only on histopathological images, with the highest AUC of 0.971. Our findings suggest that histopathological images could be used with reasonable accuracy to predict TMB levels in gastric cancer patients, while multimodal deep learning could achieve even higher levels of accuracy. This study sheds new light on predicting TMB in gastric cancer patients.

Behavior of organic components and the migration of heavy metals during sludge dewatering by different advanced oxidation processes via optical spectroscopy and molecular fingerprint analysis.

A comparative study of the different advanced oxidation processes (Fe(II)-Oxone, Fe(II)-H2O2, and Fe(II)-NaClO) was carried out herein to analyze the characteristics of organic components and the migration of heavy metals in waste activated sludge. With the Fe(II)-Oxone and Fe(II)-H2O2 treatments, sludge dewaterability was significantly improved, however, sludge dewaterability was deteriorated by the Fe(II)-NaClO treatment. The enhanced sludge dewaterability by the Fe(II)-Oxone and Fe(II)-H2O2 treatments was strongly correlated with the shifted organic components, particularly proteins, in soluble extracellular polymeric substances (S-EPS), while the deteriorated sludge dewaterability by the Fe(II)-NaClO treatment was strongly correlated with the over release of organic components from bound EPS (B-EPS) to S-EPS. For both the Fe(II)-Oxone and Fe(II)-H2O2 treatments, the radicals preferentially attacked humic acid-like organic components over the protein-like organic components in S-EPS, while for the Fe(II)-NaClO treatment, interestingly, the radicals preferentially attacked the protein-like organic components in both S-EPS and B-EPS. The hydrophilic functional groups like phenolic OH and CO of polysaccharides may be more preferentially migrated to S-EPS of sludge by the Fe(II)-NaClO treatment compared to the other two treatments. With the Fe(II)-Oxone and Fe(II)-H2O2 treatments, the proportion of aliphatic compounds as well as the much oxygenated organic components with a low desaturation and a low molecular weight increased. While with the Fe(II)-NaClO treatment, the proportion of low oxygenated organic components with a high desaturation and a high molecular weight increased. The concentration of total organic carbon, particularly the concentration of proteins, may be the key factor determining the shift of Zn and Cu from sludge solid to liquid phase, along with the high oxidation extent of organic components and close binding to CHOS and CHON compounds as indicated by density functional theory (DFT) calculation. This study systematically revealed the simultaneous sludge dewatering and migration of heavy metals when the role of organic components was factored into herein.

Exploring binding positions and backbone conformations of peptide ligands of proteins with a backbone-centred statistical energy function.

When designing peptide ligands based on the structure of a protein receptor, it can be very useful to narrow down the possible binding positions and bound conformations of the ligand without the need to choose its amino acid sequence in advance. Here, we construct and benchmark a tool for this purpose based on a recently reported statistical energy model named SCUBA (Sidechain-Unknown Backbone Arrangement) for designing protein backbones without considering specific amino acid sequences. With this tool, backbone fragments of different local conformation types are generated and optimized with SCUBA-driven stochastic simulations and simulated annealing, and then ranked and clustered to obtain representative backbone fragment poses of strong SCUBA interaction energies with the receptor. We computationally benchmarked the tool on 111 known protein-peptide complex structures. When the bound ligands are in the strand conformation, the method is able to generate backbone fragments of both low SCUBA energies and low root mean square deviations from experimental structures of peptide ligands. When the bound ligands are helices or coils, low-energy backbone fragments with binding poses similar to experimental structures have been generated for approximately 50% of benchmark cases. We have examined a number of predicted ligand-receptor complexes by atomistic molecular dynamics simulations, in which the peptide ligands have been found to stay at the predicted binding sites and to maintain their local conformations. These results suggest that promising backbone structures of peptides bound to protein receptors can be designed by identifying outstanding minima on the SCUBA-modeled backbone energy landscape.

Efficacy and Safety of Anlotinib in Overall and Disease-Specific Advanced Gynecological Cancer: A Real-World Study.

Purpose: Anlotinib is a novel oral small-molecule multi-target tyrosine kinase inhibitor that has been approved for treating non-small cell lung cancer. However, its efficacy and safety among patients with advanced gynecological cancer have not been comprehensively evaluated. We conducted this study to address this issue in the real-world setting. Patients and Methods: Data from patients treated with Anlotinib for persistent, recurrent or metastatic gynecological cancer were collected from 17 centers from August 2018. The database lock-time was on March 2022. Anlotinib was administered orally on days 1-14 every 3 weeks until disease progression, severe toxicity occurred, or death. In this study, disease-specific advanced gynecological cancer was mainly referred to cervical, endometrial, and ovarian cancer. The outcomes included objective response rate (ORR), disease control rate (DCR), and progression-free survival (PFS). Results: A total of 249 patients were analyzed, with a median follow-up of 14.5 months. The overall ORR and DCR were 28.1% [95% confidence interval (CI) 22.6% to 34.1%] and 80.7% (95% CI 75.3% to 85.4%), respectively. Specifically, the ORR varied from 19.7% to 34.4% and the DCR differed from 81.7% to 90.0% in disease-specific advanced gynecological cancer. The median PFS was 6.1 months and ranged from 5.6 to 10.0 months in the overall and disease-specific advanced gynecological cancer, respectively. Larger cumulative dosage of Anlotinib (>700 mg) was in general associated with longer PFS in the overall and disease-specific advanced gynecological cancer. The most common adverse event related to Anlotinib treatment was pain/arthralgia (18.3%). Conclusion: In conclusion, Anlotinib holds promise in treating patients with advanced gynecological cancer including its disease-specific types, with reasonable efficacy and tolerable safety.