UBR2 targets myosin heavy chain IIb and IIx for degradation: Molecular mechanism essential for cancer-induced muscle wasting.

Cancer cachexia is a lethal metabolic syndrome featuring muscle wasting with preferential loss of fast-twitching muscle mass through an undefined mechanism. Here, we show that cancer induces muscle wasting by selectively degrading myosin heavy chain (MHC) subtypes IIb and IIx through E3 ligase UBR2-mediated ubiquitylation. Induction of MHC loss and atrophy in C2C12 myotubes and mouse tibialis anterior (TA) by murine cancer cells required UBR2 up-regulation by cancer. Genetic gain or loss of UBR2 function inversely altered MHC level and muscle mass in TA of tumor-free mice. UBR2 selectively interacted with and ubiquitylated MHC-IIb and MHC-IIx through its substrate recognition and catalytic domain, respectively, in C2C12 myotubes. Elevation of UBR2 in muscle of tumor-bearing or free mice caused loss of MHC-IIb and MHC-IIx but not MHC-I and MHC-IIa or other myofibrillar proteins, including α-actin, troponin, tropomyosin, and tropomodulin. Muscle-specific knockout of UBR2 spared KPC tumor-bearing mice from losing MHC-IIb and MHC-IIx, fast-twitching muscle mass, cross-sectional area, and contractile force. The rectus abdominis (RA) muscle of patients with cachexia-prone cancers displayed a selective reduction of MHC-IIx in correlation with higher UBR2 levels. These data suggest that UBR2 is a regulator of MHC-IIb/IIx essential for cancer-induced muscle wasting, and that therapeutic interventions can be designed by blocking UBR2 up-regulation by cancer.

Deep learning-based advances and applications for single-cell RNA-sequencing data analysis.

The rapid development of single-cell RNA-sequencing (scRNA-seq) technology has raised significant computational and analytical challenges. The application of deep learning to scRNA-seq data analysis is rapidly evolving and can overcome the unique challenges in upstream (quality control and normalization) and downstream (cell-, gene- and pathway-level) analysis of scRNA-seq data. In the present study, recent advances and applications of deep learning-based methods, together with specific tools for scRNA-seq data analysis, were summarized. Moreover, the future perspectives and challenges of deep-learning techniques regarding the appropriate analysis and interpretation of scRNA-seq data were investigated. The present study aimed to provide evidence supporting the biomedical application of deep learning-based tools and may aid biologists and bioinformaticians in navigating this exciting and fast-moving area.

Multiomics integration-based molecular characterizations of COVID-19.

The coronavirus disease 2019 (COVID-19) pandemic, caused by the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), rapidly became a global health challenge, leading to unprecedented social and economic consequences. The mechanisms behind the pathogenesis of SARS-CoV-2 are both unique and complex. Omics-scale studies are emerging rapidly and offer a tremendous potential to unravel the puzzle of SARS-CoV-2 pathobiology, as well as moving forward with diagnostics, potential drug targets, risk stratification, therapeutic responses, vaccine development and therapeutic innovation. This review summarizes various aspects of understanding multiomics integration-based molecular characterizations of COVID-19, which to date include the integration of transcriptomics, proteomics, genomics, lipidomics, immunomics and metabolomics to explore virus targets and developing suitable therapeutic solutions through systems biology tools. Furthermore, this review also covers an abridgment of omics investigations related to disease pathogenesis and virulence, the role of host genetic variation and a broad array of immune and inflammatory phenotypes contributing to understanding COVID-19 traits. Insights into this review, which combines existing strategies and multiomics integration profiling, may help further advance our knowledge of COVID-19.

Resource regulation and green innovation: Evidence from China's water rights trading policy.

Green innovation is the pivotal part connected with achieving urban sustainable development. Resource regulation, represented by water rights trading policy (WRTP), is playing an increasingly important role in supporting urban green innovation (UGI). Therefore, this paper uses the WRTP conducted in 2014 in China as a quasi-natural experiment to evaluate the net effect of resource regulation on green innovation by the identification methods of difference-in-differences (DID) model. The results show that: (1) WRTP promotes UGI significantly, and this finding holds after a series of robustness tests. (2)The influencing mechanisms are mainly by optimizing industrial structure and enhancing the efficiency of resource allocation and information exchange efficiency and the promoting function is moderated by urban competitiveness and population agglomeration. (3) The promotion effects are greater in cities with higher level of government intervention, environmental pollution, water using quantity and local economy.(4) WRTP has the spatial spillover effect on UGI. These findings provide insights into sustainable development of water resources, management of water trading market, urban green innovation and sustainable economic development.

Causal relationships between atopic dermatitis and psychiatric disorders: a bidirectional two-sample Mendelian randomization study.

BACKGROUND: Observational studies have suggested the potential associations between atopic dermatitis (AD) and psychiatric disorders. However, the causal relationship between them remains uncertain. This study aimed to evaluate the potential bidirectional causal relationship between AD and psychiatric disorders, including autism spectrum disorder (ASD), major depressive disorder (MDD), attention deficit hyperactivity disorder (ADHD), bipolar disorder (BD), anorexia nervosa (AN), Tourette syndrome (TS), schizophrenia, and anxiety. METHODS: Bidirectional two-sample Mendelian randomization (MR) was employed to elucidate the causality between AD and psychiatric disorders, using summary statistics from the most comprehensive genome-wide association studies conducted on AD (Ncases = 60,653, Ncontrols = 804,329). Psychiatric disorders were derived from the Psychiatric Genomics Consortium and were independent of AD data sources. The MR analysis entailed the implementation of multiple methods, including the inverse variance weighted method, MR-Egger regression method, weighted median method, simple mode method, and weighted mode method. RESULTS: Bidirectional two-sample MR analysis uncovered significant causal associations between AD and severe psychiatric disorders. Specifically, liability to AD was associated with increased risk of ADHD (OR = 1.116; 95% CI: [1.009, 1.234]; P = 0.033) and ASD (OR = 1.131; 95% CI: [1.023, 1.251]; P = 0.016). Additionally, evidence suggested that liability to ADHD (OR = 1.112; 95% CI: [1.094, 1.130]; P = 9.20e-40), liability to AN (OR = 1.1; 95% CI: [1.068, 1.134]; P = 4.45e-10) and liability to BD (OR = 1.067; 95% CI: [1.009, 1.128]; P = 0.023) were associated with an increased risk of AD. Only the causal association between AD and ASD was independent of the reverse effect bias. These causal associations were robust and not affected by biases of heterogeneity and horizontal pleiotropy. CONCLUSIONS: Our study emphasizes the significant causal association between AD and an increased risk of ASD, and also identifying BD and AN as risk factors for AD.

Light-Responsive Supramolecular Liquid-Crystalline Metallacycle for Orthogonal Multimode Photopatterning.

The development of multimode photopatterning systems based on supramolecular coordination complexes (SCCs) is considerably attractive in supramolecular chemistry and materials science, because SCCs can serve as promising platforms for the incorporation of multiple functional building blocks. Herein, we report a light-responsive liquid-crystalline metallacycle that is constructed by coordination-driven self-assembly. By exploiting its fascinating liquid crystal features, bright emission properties, and facile photocyclization capability, a unique system with spatially-controlled fluorescence-resonance energy transfer (FRET) is built through the introduction of a photochromic spiropyran derivative, which led to the realization of the first example of a liquid-crystalline metallacycle for orthogonal photopatterning in three-modes, namely holography, fluorescence, and photochromism.

Molecular Bridging Induced Anti-salting-out Effect Enabling High Ionic Conductive ZnSO4-based Hydrogel for Quasi-solid-state Zinc Ion Batteries.

Hydrogel electrolytes (HEs) hold great promise in tackling severe issues emerging in aqueous zinc-ion batteries, but the prevalent salting-out effect of kosmotropic salt causes low ionic conductivity and electrochemical instability. Herein, a subtle molecular bridging strategy is proposed to enhance the compatibility between PVA and ZnSO4 from the perspective of hydrogen-bonding microenvironment re-construction. By introducing urea containing both an H-bond acceptor and donor, the broken H-bonds between PVA and H2O, initiated by the SO42--driven H2O polarization, could be re-united via intense intermolecular hydrogen bonds, thus leading to greatly increased carrying capacity of ZnSO4. The urea-modified PVA-ZnSO4 HEs featuring a high ionic conductivity up to 31.2 mS cm-1 successfully solves the sluggish ionic transport dilemma at the solid-solid interface. Moreover, an organic solid-electrolyte-interphase can be derived from the in-situ electro-polymerization of urea to prohibit H2O-involved side reactions, thereby prominently improving the reversibility of Zn chemistry. Consequently, Zn anodes witness an impressive lifespan extension from 50 h to 2200 h at 0.1 mA cm-2 while the Zn-I2 full battery maintains a remarkable Coulombic efficiency (>99.7%) even after 8000 cycles. The anti-salting-out strategy proposed in this work provides an insightful concept for addressing the phase separation issue of functional HEs.

A hybrid algorithm for clinical decision support in precision medicine based on machine learning.

PURPOSE: The objective of the manuscript is to propose a hybrid algorithm combining the improved BM25 algorithm, k-means clustering, and BioBert model to better determine biomedical articles utilizing the PubMed database so, the number of retrieved biomedical articles whose content contains much similar information regarding a query of a specific disease could grow larger. DESIGN/METHODOLOGY/APPROACH: In the paper, a two-stage information retrieval method is proposed to conduct an improved Text-Rank algorithm. The first stage consists of employing the improved BM25 algorithm to assign scores to biomedical articles in the database and identify the 1000 publications with the highest scores. The second stage is composed of employing a method called a cluster-based abstract extraction to reduce the number of article abstracts to match the input constraints of the BioBert model, and then the BioBert-based document similarity matching method is utilized to obtain the most similar search outcomes between the document and the retrieved morphemes. To realize reproducibility, the written code is made available on https://github.com/zzc1991/TREC_Precision_Medicine_Track . FINDINGS: The experimental study is conducted based on the data sets of TREC2017 and TREC2018 to train the proposed model and the data of TREC2019 is used as a validation set confirming the effectiveness and practicability of the proposed algorithm that would be implemented for clinical decision support in precision medicine with a generalizability feature. ORIGINALITY/VALUE: This research integrates multiple machine learning and text processing methods to devise a hybrid method applicable to domains of specific medical literature retrieval. The proposed algorithm provides a 3% increase of P@10 than that of the state-of-the-art algorithm in TREC 2019.

Computational principles and practice for decoding immune contexture in the tumor microenvironment.

Tumor-infiltrating immune cells (TIICs) have been recognized as crucial components of the tumor microenvironment (TME) and induced both beneficial and adverse consequences for tumorigenesis as well as outcome and therapy (particularly immunotherapy). Computer-aided investigation of immune cell components in the TME has become a promising avenue to better understand the interplay between the immune system and tumors. In this study, we presented an overview of data sources, computational methods and software tools, as well as their application in inferring the composition of tumor-infiltrating immune cells in the TME. In parallel, we explored the future perspectives and challenges that may be faced with more accurate quantitative infiltration of immune cells in the future. Together, our study provides a little guide for scientists in the field of clinical and experimental immunology to look for dedicated resources and more competent tools for accelerating the unraveling of tumor-immune interactions with the implication in precision immunotherapy.

Mechanistically derived patient-level framework for precision medicine identifies a personalized immune prognostic signature in high-grade serous ovarian cancer.

An accurate prognosis assessment for cancer patients could aid in guiding clinical decision-making. Reliance on traditional clinical features alone in a complex clinical environment is challenging and unsatisfactory in the era of precision medicine; thus, reliable prognostic biomarkers are urgently required to improve a patient staging system. In this study, we proposed a patient-level computational framework from mechanistic and translational perspectives to establish a personalized prognostic signature (named PLPPS) in high-grade serous ovarian carcinoma (HGSOC). The PLPPS composed of 68 immune genes achieved accurate prognostic risk stratification for 1190 patients in the meta-training cohort and was rigorously validated in multiple cross-platform independent cohorts comprising 792 HGSOC patients. Furthermore, the PLPPS was shown to be the better prognostic factor compared with clinical parameters in the univariate analysis and retained a significant independent association with prognosis after adjusting for clinical parameters in the multivariate analysis. In benchmark comparisons, the performance of PLPPS (hazard ratio (HR), 1.371; concordance index (C-index), 0.604 and area under the curve (AUC), 0.637) is comparable to or better than other published gene signatures (HR, 0.972 to 1.340; C-index, 0.495 to 0.592 and AUC, 0.48-0.624). With further validation in prospective clinical trials, we hope that the PLPPS might become a promising genomic tool to guide personalized management and decision-making of HGSOC in clinical practice.

Computational recognition of lncRNA signature of tumor-infiltrating B lymphocytes with potential implications in prognosis and immunotherapy of bladder cancer.

Long noncoding RNAs (lncRNAs) have been associated with cancer immunity regulation and the tumor microenvironment (TME). However, functions of lncRNAs of tumor-infiltrating B lymphocytes (TIL-Bs) and their clinical significance have not yet been fully elucidated. In the present study, a machine learning-based computational framework is presented for the identification of lncRNA signature of TIL-Bs (named 'TILBlncSig') through integrative analysis of immune, lncRNA and clinical profiles. The TILBlncSig comprising eight lncRNAs (TNRC6C-AS1, WASIR2, GUSBP11, OGFRP1, AC090515.2, PART1, MAFG-DT and LINC01184) was identified from the list of 141 B-cell-specific lncRNAs. The TILBlncSig was capable of distinguishing worse compared with improved survival outcomes across different independent patient datasets and was also independent of other clinical covariates. Functional characterization of TILBlncSig revealed it to be an indicator of infiltration of mononuclear immune cells (i.e. natural killer cells, B-cells and mast cells), and it was associated with hallmarks of cancer, as well as immunosuppressive phenotype. Furthermore, the TILBlncSig revealed predictive value for the survival outcome and immunotherapy response of patients with anti-programmed death-1 (PD-1) therapy and added significant predictive power to current immune checkpoint gene markers. The present study has highlighted the value of the TILBlncSig as an indicator of immune cell infiltration in the TME from a noncoding RNA perspective and strengthened the potential application of lncRNAs as predictive biomarkers of immunotherapy response, which warrants further investigation.

Multi-dimensional characterization of immunological profiles in small cell lung cancer uncovers clinically relevant immune subtypes with distinct prognoses and therapeutic vulnerabilities.

Small-cell lung cancer (SCLC) is generally considered a 'homogenous' disease, with little documented inter-tumor heterogeneity in treatment guidance or prognosis evaluation. The precise identification of clinically relevant molecular subtypes remains incomplete and their translation into clinical practice is limited. In this retrospective cohort study, we comprehensively characterized the immune microenvironment in SCLC by integrating transcriptional and protein profiling of formalin-fixation-and-paraffin-embedded (FFPE) samples from 29 patients. We identified two distinct disease subtypes: immune-enriched (IE-subtype) and immune-deprived (ID-subtype), displaying heterogeneity in immunological, biological, and clinical features. The IE-subtype was characterized by abundant immune infiltrate and elevated levels of interferon-alpha/gamma (IFNα/IFNγ) and inflammatory response, while the ID-subtype featured a complete lack of immune infiltration and a more proliferative phenotype. These two immune subtypes are associated with clinical benefits in SCLC patients treated with adjuvant therapy, with the IE-subtype exhibiting a more favorable response leading to improved survival and reduced disease recurrence risk. Additionally, we identified and validated a personalized prognosticator of immunophenotyping, the CCL5/CXCL9 chemokine index (CCI), using machine learning. The CCI demonstrated superior predictive abilities for prognosis and clinical benefits in SCLC patients, validated in our institute immunohistochemistry cohort and multicenter bulk transcriptomic data cohorts. In conclusion, our study provides a comprehensive and multi-dimensional characterization of the immune architecture of SCLC using clinical FFPE samples and proposes a new immune subtyping conceptual framework enabling risk stratification and the appropriate selection of individualized therapy.

Effect of external electric field on the electronic properties of the AlAs/SiC van der Waals heterostructure.

Type-II van der Waals (vdW) heterostructures are regarded as the optimum candidates for unipolar electronic device applications due to their capacity for spontaneous electron-hole separation. Here, we studied the electronic properties of the AlAs/SiC vdW heterostructure via density functional theory calculations. Results show that the conduction band minimum (CBM) and valence band maximum (VBM) of this heterostructure are mainly contributed by different materials, illustrating that the AlAs/SiC heterostructure has a type-II band alignment. Interestingly, this heterostructure possesses flat valence bands near the Fermi level. In addition, under the modulation of external electric field ranging between -1 V Å-1∼0.8 V Å-1, the band gap of the heterostructure can be tuned continuously, while the band structure maintains a stable type-II band alignment with flat top valence bands. When the electric field exceeds -1 or 0.8 V Å-1, the heterostructure transitions from semiconductor material to metal, indicating the tunability of electronic properties under external fields. These results indicate that the AlAs/SiC heterostructure shows great potential for application in high-performance optoelectronic devices and a strong correlation may exist in this system.

Immuno-genomic characterisation of high-grade serous ovarian cancer reveals immune evasion mechanisms and identifies an immunological subtype with a favourable prognosis and improved therapeutic efficacy.

BACKGROUND: Immunotherapy has revolutionised the field of cancer therapy and immunology, but has demonstrated limited therapeutic efficacy in high-grade serous ovarian cancer (HGSOC). METHODS: Multi-omics data of 495 TCGA HGSOC tumours and RNA-seq data of 1708 HGSOC tumours were analyzed. Multivariate Cox regression analysis and meta-analyses were used to identify prognostic genes. The immune microenvironment was characterised using the ssGSEA methods for 28 immune cell types. Immunohistochemistry staining of tumour tissues of 14 patients was used to validate the key findings further. RESULTS: A total of 1142 genes were identified as favourable prognostic genes, which are prevailing in immune-related pathways and the infiltration of most immune subpopulations was observed to be associated with a favourable prognosis suggesting that tumour immunogenicity was the most prominent factor associated with improved clinical outcomes and response to chemotherapy of HGSOC. We identified multiple genomic and transcriptomic determinants of immunogenicity, including the copy loss of chromosome 4q and deficiencies of the homologous recombination pathway. Finally, an immunological subtype characterised by increased infiltration of activated CD8 T cells and decreased Tregs was associated with favourable prognosis and improved therapeutic efficacy. CONCLUSIONS: Our study characterised the immunogenomic landscape and refined the immunological classifications of HGSOC. This may improve the selection of patients with HGSOC who are suitable candidates for immunotherapy.

Computational identification of mutator-derived lncRNA signatures of genome instability for improving the clinical outcome of cancers: a case study in breast cancer.

Emerging evidence revealed the critical roles of long non-coding RNAs (lncRNAs) in maintaining genomic instability. However, identification of genome instability-associated lncRNAs and their clinical significance in cancers remain largely unexplored. Here, we developed a mutator hypothesis-derived computational frame combining lncRNA expression profiles and somatic mutation profiles in a tumor genome and identified 128 novel genomic instability-associated lncRNAs in breast cancer as a case study. We then identified a genome instability-derived two lncRNA-based gene signature (GILncSig) that stratified patients into high- and low-risk groups with significantly different outcome and was further validated in multiple independent patient cohorts. Furthermore, the GILncSig correlated with genomic mutation rate in both ovarian cancer and breast cancer, indicating its potential as a measurement of the degree of genome instability. The GILncSig was able to divide TP53 wide-type patients into two risk groups, with the low-risk group showing significantly improved outcome and the high-risk group showing no significant difference compared with those with TP53 mutation. In summary, this study provided a critical approach and resource for further studies examining the role of lncRNAs in genome instability and introduced a potential new avenue for identifying genomic instability-associated cancer biomarkers.

Improved Reversible Zinc Storage Achieved in a Constitutionally Crystalline-Stable Mn(VO3 )2 Nanobelts Cathode.

Rechargeable zinc-ion batteries (ZIBs) are potential for grid-scale applications owing to their safety, low price, and available sources. The development of ZIBs cathode with high specific capacity, wide operating voltage window and stable cyclability is urgently needed in next-generation commercial batteries. Herein, we report a structurally crystalline-stable Mn(VO3 )2 nanobelts cathode for ZIBs prepared via a facile hydrothermal method. The as-synthesized Mn(VO3 )2 exhibited high specific capacity of 350 mAh g-1 at 0.1 A g-1 , and maintained a capacity retention of 92 % after 10,000 cycles at 2 A g-1 . It also showed good rate performance and obtained a reversible capacity of up to 200 mAh g-1 after 600 cycles at 0.2 A g-1 under -20 °C. The electrochemical tests suggest that Mn(VO3 )2 nanobelts impart fast Zn2+ ions migration, and the introduction of manganese atoms help make the structures more indestructible, leading to a good rate performance and prolonged cycle lifespan.

Cadmium, lead and arsenic contamination in an abandoned nonferrous metal smelting site in southern China: Chemical speciation and mobility.

The investigation of chemical speciation of primary toxic metal(loid)s (Cd, Pb, and As) in soil profile in nonferrous metal smelting site is a key to the assessment of their mobility characteristics and formulation of subsequent remediation strategy. In this study, 74 soil samples were collected at 12 different soil profiles; soil physio-chemical properties and total content of Cd, Pb and As and corresponding chemical speciation were also determined. The results showed that the mean total concentration followed the order of Pb > As > Cd. A large proportion of Pb, Cd and As were accumulated in upper soil profiles (depth < 3 m). Heavy pollution of Pb, Cd and As were observed in the whole soil profile at the area of fuel oil storage tank (ZY6) and lead smelting area (ZY8). The dominant fraction of Cd was exchangeable fraction (F1); Pb was dominant in Fe/Mn oxides-bound fraction (F3) in most cases; Crystallized Fe/Al hydrous oxides bound fraction (F4) generally accounted for a large proportion of As. Mobility factor (MF) followed the order Cd > As > Pb, indicating that Cd was the most mobile element in soil profiles. Pearson correlation analysis found that MFCd was significantly positively correlated to soil silt; the F4 fraction percentage of As was significantly positively correlated to soil redox potential (Eh). Additionally, MFCd/Pb was found to be positively correlated to crystalline iron (Fec), while negatively correlated to amorphous iron (Feo). The findings reported in this study, on the basis of distribution characteristics of chemical speciation could provide a new solution for future soil remediation at the site. Long-term solutions to metal(loid)s pollution might be offered by microbial-assisted soil washing technique that promotes the transformation of Fe/Mn oxides-bound fraction and organic/sulfide-bound fraction.

Inter-observer variations of the tumor bed delineation for patients after breast conserving surgery in preoperative magnetic resonance and computed tomography scan fusion.

PURPOSE: Tumor bed (TB) delineation based on preoperative magnetic resonance imaging (pre-MRI) fused with postoperative computed tomography (post-CT) were compared to post-CT only to define pre-MRI may aid in improving the accuracy of delineation. METHODS AND MATERIALS: The pre-MRI imaging of 10 patients underwent radiotherapy (RT) after breast conserving surgery (BCS) were reviewed. Post-CT scans were acquired in the same prone position as pre-MRI. Pre-MRI and post-CT automatically match and then manual alignment was given to enhance fusion consistency. Three radiation oncologists and 2 radiologists delineated the clinical target volume (CTV) for CT-based. The gross target volume (GTV) of pre-MRI-based was determined by the volume of tumor acquired with 6 sequences: T1, T2, T2W-SPAIR, DWI, dyn-eTHRIVE and sdyn-eTHRIVE, expended 10 mm to form the CTV-pre-MRI. Planning target volume (PTV) for each sequence was determined by CTV extended 15 mm, trimmed to 3 mm from skin and the breast-chest wall interface. The variability of the TB delineation were developed as follows: the mean volume, conformity index (CI) and dice coefficient (DC). RESULTS: The mean volumes of CTV and PTV delineated with CT were all larger than those with pre-MRI. The lower inter-observer variability was observed from PTV, especially in sdyn-eTHRIVE in all sequences. For each sequence of pre-MRI, all DCs were larger than post-CT, and the largest DC was observed by sdyn-eTHRIVE sequence fusion to post-CT. The overlap for PTV was significantly improved in the pre-MRI-based compared with the CT-based. CONCLUSIONS: TB volumes based on pre-MRI were smaller than post-CT with CVS increased. Pre-MRI provided a more precise definition of the TB with observers performed a smaller inter-observer variability than CT. Pre-MRI, especially in sdyn-eTHRIVE sequence, should help in reducing treatment volumes with the improved accuracy of TB delineation of adjuvant RT of breast cancer.

Sex and Pubertal Status Influence Dendritic Spine Density on Frontal Corticostriatal Projection Neurons in Mice.

In humans, nonhuman primates, and rodents, the frontal cortices exhibit grey matter thinning and dendritic spine pruning that extends into adolescence. This maturation is believed to support higher cognition but may also confer psychiatric vulnerability during adolescence. Currently, little is known about how specific cell types in the frontal cortex mature or whether puberty plays a role in the maturation of some cell types but not others. Here, we used mice to characterize the spatial topography and adolescent development of cross-corticostriatal (cSTR) neurons that project through the corpus collosum to the dorsomedial striatum. We found that apical spine density on cSTR neurons in the medial prefrontal cortex decreased significantly between late juvenile (P29) and young adult time points (P60), with females exhibiting higher spine density than males at both ages. Adult males castrated prior to puberty onset had higher spine density compared to sham controls. Adult females ovariectomized before puberty onset showed greater variance in spine density measures on cSTR cells compared to controls, but their mean spine density did not significantly differ from sham controls. Our findings reveal that these cSTR neurons, a subtype of the broader class of intratelencephalic-type neurons, exhibit significant sex differences and suggest that spine pruning on cSTR neurons is regulated by puberty in male mice.

Associations of environment and lifestyle factors with suboptimal health status: a population-based cross-sectional study in urban China.

INTRODUCTION: Suboptimal health status (SHS), an intermediate state between chronic disease and health, is characterized by chronic fatigue, non-specific pain, headaches, dizziness, anxiety, depression, and functional system disorders with a high prevalence worldwide. Although some lifestyle factors (e.g. smoking, alcohol consumption, physical exercise) and environmental factors (e.g. air quality, noise, living conditions) have already been studied, few studies can comprehensively illustrate the associations of lifestyle and environment factors with general, physical, mental, and social SHS. METHODS: A cross-sectional study was conducted among 6750 urban residents aged 14 years or over in five random cities from September 2017 to September 2018 through face-to-face questionnaires. There were 5881 valid questionnaires with a response rate of 87%. A general linear model and structural equation model were developed to quantify the effects of lifestyle behaviors and environment factors on SHS. RESULTS: The detection rates of general, physical, mental, and social SHS were 66.7, 67.0, 65.5, and 70.0%, respectively. Good lifestyle behaviors and favorable environment factors positively affected SHS (P < 0.001). Lifestyle behaviors had the largest effect on physical SHS (ß = - 0.418), but the least on social SHS (ß = - 0.274). Environment factors had the largest effect on mental SHS (ß = 0.286), but the least on physical SHS (ß = 0.225). CONCLUSIONS: Lifestyle behaviors and environment factors were important influencing factors of SHS. Physical SHS was more associated with lifestyle. Lifestyle and environment were similarly associated with mental and social SHS.