Understanding the subtypes of non-suicidal self-injury: A new conceptual framework based on a systematic review.

Non-suicidal self-injury (NSSI) is a significant public health problem, but there is no consistent evidence of its risk factors. One possibility is that there are subtypes NSSI that have different risk factors and clinical symptoms. In this review we evaluated the evidence of subtypes to determine if there were consistent subtypes of NSSI that emerged across studies. Four databases (Medline; Embase; PsycINFO; Web of Science) were searched to identify studies that used data-driven approaches and were published before November 9, 2022. There were 21 studies with 23 unique samples for review. Most of the included studies used NSSI symptoms or personal characteristics as the subtyping indicators, revealing 2-5 subtypes of NSSI. Variations in subtyping indicators, sample characteristics, and statistical methods may have contributed to the inconsistent number and characteristics of subtypes across studies. A new conceptual framework is proposed to integrate these diverse findings, highlighting the important roles of NSSI function and psychological pain in differentiating NSSI subtypes. This framework sheds light on the differences among self-injurers and offers insights for future endeavors to address the complexities of NSSI.

Quantifying the Importance of Non-Suicidal Self-Injury Characteristics in Predicting Different Clinical Outcomes: Using Random Forest Model.

Existing research on non-suicidal self-injury (NSSI) among adolescents has primarily concentrated on general risk factors, leaving a significant gap in understanding the specific NSSI characteristics that predict diverse psychopathological outcomes. This study aims to address this gap by using Random Forests to discern the significant predictors of different clinical outcomes. The study tracked 348 adolescents (64.7% girls; mean age = 13.31, SD = 0.91) over 6 months. Initially, 46 characteristics of NSSI were evaluated for their potential to predict the repetition of NSSI, as well as depression, anxiety, and suicidal risks at a follow-up (T2). The findings revealed distinct predictors for each psychopathology. Specifically, psychological pain was identified as a significant predictor for depression, anxiety, and suicidal risks, while the perceived effectiveness of NSSI was crucial in forecasting its repetition. These findings imply that it is feasible to identify high-risk individuals by assessing key NSSI characteristics, and also highlight the importance of considering diverse NSSI characteristics when working with self-injurers.

LncRNA DANA1 promotes drought tolerance and histone deacetylation of drought responsive genes in Arabidopsis.

Although many long noncoding RNAs have been discovered in plants, little is known about their biological function and mode of action. Here we show that the drought-induced long intergenic noncoding RNA DANA1 interacts with the L1p/L10e family member protein DANA1-INTERACTING PROTEIN 1 (DIP1) in the cell nucleus of Arabidopsis, and both DANA1 and DIP1 promote plant drought resistance. DANA1 and DIP1 increase histone deacetylase HDA9 binding to the CYP707A1 and CYP707A2 loci. DIP1 further interacts with PWWP3, a member of the PEAT complex that associates with HDA9 and has histone deacetylase activity. Mutation of DANA1 enhances CYP707A1 and CYP707A2 acetylation and expression resulting in impaired drought tolerance, in agreement with dip1 and pwwp3 mutant phenotypes. Our results demonstrate that DANA1 is a positive regulator of drought response and that DANA1 works jointly with the novel chromatin-related factor DIP1 on epigenetic reprogramming of the plant transcriptome during the response to drought.

Protocol for detecting lncRNA-protein interactions in vitro by tRSA RNA pull-down assay.

Long non-coding RNAs (lncRNAs) work together with diverse RNA-binding proteins (RBPs) to fulfill key regulations in important cellular functions. Here, we present a protocol to detect lncRNA-RBP interactions in vitro using a tRNA scaffold containing a streptavidin aptamer pull-down assay. We describe steps for preparing both protein and lncRNA transcripts, lncRNA-protein interaction detection with an in vitro binding assay, and western blot analysis. This protocol is applicable to screen for RNA-interacting proteins using cell lysates followed by mass spectrometry analysis. For complete details on the use and execution of this protocol, please refer to Yang et al. (2023).1.

Integrated Bulk and Single-cell RNA Sequencing Data Constructs and Validates a Prognostic Model for Non-small Cell Lung Cancer.

Background: Most of the current research on prognostic model construction for non-small cell lung cancer (NSCLC) only involves in bulk RNA-seq data without integration of single-cell RNA-seq (scRNA-seq) data. Besides, most of the prognostic models are constructed by predictive genes, ignoring other predictive variables such as clinical features. Methods: We obtained scRNA-seq data from GEO database and bulk RNA-seq data from TCGA database. We construct a prognostic model through the Least Absolute Shrinkage and Selection Operator (LASSO) and Cox regression. Furthermore, we performed ESTIMATE, CIBERSORT, immune checkpoint-related analyses and compared drug sensitivity using pRRophetic method judged by IC50 between different risk groups. Results: 14 tumor-related genes were extracted for model construction. The AUC for 1-, 3-, and 5 years overall survival prediction in TCGA and three validation cohorts were almost higher than 0.65, some of which were even higher than 0.7, even 0.8. Besides, calibration curves suggested no departure between model prediction and perfect fit. Additionally, immune-related and drug sensitivity results revealed potential targets and strategies for treatment, which can provide clinical guidance. Conclusion: We integrated traditional bulk RNA-seq and scRNA-seq data, along with predictive clinical features to develop a prognostic model for patients with NSCLC. According to the constructed model, patients in different groups can follow precise and individual therapeutic schedules based on immune characteristics as well as drug sensitivity.

Optimizing dose-schedule regimens with bayesian adaptive designs: opportunities and challenges.

Due to the small sample sizes in early-phase clinical trials, the toxicity and efficacy profiles of the dose-schedule regimens determined for subsequent trials may not be well established. The recent development of novel anti-tumor treatments and combination therapies further complicates the problem. Therefore, there is an increasing recognition of the essential place of optimizing dose-schedule regimens, and new strategies are now urgently needed. Bayesian adaptive designs provide a potentially effective way to evaluate several doses and schedules simultaneously in a single clinical trial with higher efficiency, but real-world implementation examples of such adaptive designs are still few. In this paper, we cover the critical factors associated with dose-schedule optimization and review the related innovative Bayesian adaptive designs. The assumptions, characteristics, limitations, and application scenarios of those designs are introduced. The review also summarizes some unresolved issues and future research opportunities for dose-schedule optimization.

Phylosymbiosis: The Eco-Evolutionary Pattern of Insect-Symbiont Interactions.

Insects harbor diverse assemblages of bacterial and fungal symbionts, which play crucial roles in host life history. Insects and their various symbionts represent a good model for studying host-microbe interactions. Phylosymbiosis is used to describe an eco-evolutionary pattern, providing a new cross-system trend in the research of host-associated microbiota. The phylosymbiosis pattern is characterized by a significant positive correlation between the host phylogeny and microbial community dissimilarities. Although host-symbiont interactions have been demonstrated in many insect groups, our knowledge of the prevalence and mechanisms of phylosymbiosis in insects is still limited. Here, we provide an order-by-order summary of the phylosymbiosis patterns in insects, including Blattodea, Coleoptera, Diptera, Hemiptera, Hymenoptera, and Lepidoptera. Then, we highlight the potential contributions of stochastic effects, evolutionary processes, and ecological filtering in shaping phylosymbiotic microbiota. Phylosymbiosis in insects can arise from a combination of stochastic and deterministic mechanisms, such as the dispersal limitations of microbes, codiversification between symbionts and hosts, and the filtering of phylogenetically conserved host traits (incl., host immune system, diet, and physiological characteristics).

Two new species of Sinolachnus Hille Ris Lambers (Hemiptera, Aphididae, Lachninae) from China.

Two new Sinolachnus species from China, Sinolachnusrubusis Qiao & Li, sp. nov. feeding on Rubus sp. from Shaanxi and Sichuan Provinces, and Sinolachnusyunnanensis Qiao & Li, sp. nov. feeding on Elaeagnus sp. from Yunnan Province, are described and illustrated. Keys to Sinolachnus species distributed in China are presented. All examined specimens are deposited in the National Zoological Museum of China, Institute of Zoology, Chinese Academy of Sciences, Beijing, China.

Cavariella Del Guercio (Hemiptera, Aphidinae, Macrosiphini) in China, with a new species, new synonymies, and first country records.

The genus Cavariella is distinguished from other Macrosiphini genera (Aphididae, Aphidinae) because it has a supra-caudal process on abdominal tergite VIII which possesses two setae distally. It is Holarctic in distribution, and half of its species are Asian. The Chinese fauna of this genus, 17 species, have been restudied, morphologically and through DNA barcodes. As a result: Cavariellahidaensis Takahashi is transferred to Elatobium; Cavariellasculptura Qiao & Xu, sp. nov. is described from specimens collected on Torilis and Cryptotaenia (Apiaceae); Cavariellacessana Zhang, Chen, Zhong & Li, syn. nov. and Cavariellalargispiracula Zhang, Chen, Zhong & Li, syn. nov. are respectively junior synonyms of Cavariellaaquatica (Gillette & Bragg) and Cavariellasapporoensis Takahashi; Cavariellagilgiana Zhang, Chen, Zhong & Li and Cavariellalhasana Zhang are confirmed as valid species and complete descriptions are provided; Cavariellabhutanensis Chakrabarti & Das, Cavariellanigra Basu, Cavariellapastinacae (Linnaeus), and Cavariellapustula Essig are recorded for the first time from China. Additionally, keys for species of Cavariella known in China are provided and modifications to the key by Blackman and Eastop of aphid species on Angelica (Aphids on World's Plants) are presented.

Long noncoding RNA ARTA controls ABA response through MYB7 nuclear trafficking in Arabidopsis.

In eukaryotes, transcription factors are a crucial element in the regulation of gene expression, and nuclear translocation is the key to the function of transcription factors. Here, we show that the long intergenic noncoding RNA ARTA interacts with an importin ß-like protein, SAD2, through a long noncoding RNA-binding region embedded in the carboxyl terminal, and then it blocks the import of the transcription factor MYB7 into the nucleus. Abscisic acid (ABA)-induced ARTA expression can positively regulate ABI5 expression by fine-tuning MYB7 nuclear trafficking. Therefore, the mutation of arta represses ABI5 expression, resulting in desensitization to ABA, thereby reducing Arabidopsis drought tolerance. Our results demonstrate that lncRNA can hijack a nuclear trafficking receptor to modulate the nuclear import of a transcription factor during plant responses to environmental stimuli.

BASIC: A Bayesian adaptive synthetic-control design for phase II clinical trials.

BACKGROUND: Randomized controlled trials are considered the gold standard for evaluating experimental treatments but often require large sample sizes. Single-arm trials require smaller sample sizes but are subject to bias when using historical control data for comparative inferences. This article presents a Bayesian adaptive synthetic-control design that exploits historical control data to create a hybrid of a single-arm trial and a randomized controlled trial. METHODS: The Bayesian adaptive synthetic control design has two stages. In stage 1, a prespecified number of patients are enrolled in a single arm given the experimental treatment. Based on the stage 1 data, applying propensity score matching and Bayesian posterior prediction methods, the usefulness of the historical control data for identifying a pseudo sample of matched synthetic-control patients for making comparative inferences is evaluated. If a sufficient number of synthetic controls can be identified, the single-arm trial is continued. If not, the trial is switched to a randomized controlled trial. The performance of The Bayesian adaptive synthetic control design is evaluated by computer simulation. RESULTS: The Bayesian adaptive synthetic control design achieves power and unbiasedness similar to a randomized controlled trial but on average requires a much smaller sample size, provided that the historical control data patients are sufficiently comparable to the trial patients so that a good number of matched controls can be identified in the historical control data. Compared to a single-arm trial, The Bayesian adaptive synthetic control design yields much higher power and much smaller bias. CONCLUSION: The Bayesian adaptive synthetic-control design provides a useful tool for exploiting historical control data to improve the efficiency of single-arm phase II clinical trials, while addressing the problem of bias when comparing trial results to historical control data. The proposed design achieves power similar to a randomized controlled trial but may require a substantially smaller sample size.

Seamless phase II/III design: a useful strategy to reduce the sample size for dose optimization.

BACKGROUND: The traditional more-is-better dose selection paradigm, originally developed for cytotoxic chemotherapeutics, can be problematic when applied to the development of novel molecularly targeted agents. Recognizing this issue, the US Food and Drug Administration initiated Project Optimus to reform the dose optimization and selection paradigm in oncology drug development, emphasizing the need for greater attention to benefit-risk considerations. METHODS: We identify different types of phase II/III dose-optimization designs, classified according to trial objectives and endpoint types. Through computer simulations, we examine their operating characteristics and discuss the relevant statistical and design considerations for effective dose optimization. RESULTS: Phase II/III dose-optimization designs are capable of controlling family-wise type I error rates and achieving appropriate statistical power with substantially smaller sample sizes than the conventional approach while also reducing the number of patients who experience toxicity. Depending on the design and scenario, the sample size savings range from 16.6% to 27.3%, with a mean savings of 22.1%. CONCLUSIONS: Phase II/III dose-optimization designs offer an efficient way to reduce sample sizes for dose optimization and accelerate the development of targeted agents. However, because of interim dose selection, the phase II/III dose-optimization design presents logistical and operational challenges and requires careful planning and implementation to ensure trial integrity.

Molecular networks reveal complex interactions with MSM in heterosexual women living with HIV-1 who play peripheral roles in Guangzhou, China.

BACKGROUND: With the number of newly diagnosed HIV-positive heterosexual women increasing yearly, it is urgent to understand HIV-1 transmission among heterosexual women in Guangzhou, China. METHODS: HIV-1 pol sequences were obtained from people living with HIV-1 during 2008 to 2017 in Guangzhou, China. A molecular network was constructed using HIV-1 TRAnsmission Cluster Engine with 1.5% genetic distance. Potential linkage and centrality metric were measured with Cytoscape. Transmission pathways between heterosexual women and men who have sex with men (MSM) were determined using Bayesian phylogenetic analysis. RESULTS: In the network, 1799 (62.6%) MSM, 692 (24.1%) heterosexual men and 141 (4.9%) heterosexual women formed 259 clusters. Molecular clusters including MSM and heterosexuals were more likely to form larger networks (P<0.001). Nearly half of the heterosexual women (45.4%) were linked to heterosexual men and 17.7% to MSM, but only 0.9% of MSM were linked to heterosexual women. Thirty-three (23.4%) heterosexual women linked to at least one MSM node and were in peripheral role. Compared to general heterosexual women, the proportion of heterosexual women linked to MSM infected with CRF55_01B (P<0.001) and CRF07_BC (P<0.001) was higher than that of other subtypes, and the proportion diagnosed between 2012 and 2017 (P = 0.001) was higher than that in 2008-2012. In MCC trees, 63.6% (21/33) of the heterosexual women differentiated from the heterosexual evolutionary branch, while 36.4% (12/33) differentiated from the MSM evolutionary branch. CONCLUSION: Heterosexual women living with HIV-1 were mainly linked to heterosexual men and were in peripheral positions in the molecular network. The role of heterosexual women in HIV-1 transmission was limited, but the interaction between MSM and heterosexual women were complex. Awareness of the HIV-1 infection status of sexual partners and active HIV-1 detection are needed for women.

IBIS: identify biomarker-based subgroups with a Bayesian enrichment design for targeted combination therapy.

BACKGROUND: Combination therapies directed at multiple targets have potentially improved treatment effects for cancer patients. Compared to monotherapy, targeted combination therapy leads to an increasing number of subgroups and complicated biomarker-based efficacy profiles, making it more difficult for efficacy evaluation in clinical trials. Therefore, it is necessary to develop innovative clinical trial designs to explore the efficacy of targeted combination therapy in different subgroups and identify patients who are more likely to benefit from the investigational combination therapy. METHODS: We propose a statistical tool called 'IBIS' to Identify BIomarker-based Subgroups and apply it to the enrichment design framework. The IBIS contains three main elements: subgroup division, efficacy evaluation and subgroup identification. We first enumerate all possible subgroup divisions based on biomarker levels. Then, Jensen-Shannon divergence is used to distinguish high-efficacy and low-efficacy subgroups, and Bayesian hierarchical model (BHM) is employed to borrow information within these two subsets for efficacy evaluation. Regarding subgroup identification, a hypothesis testing framework based on Bayes factors is constructed. This framework also plays a key role in go/no-go decisions and enriching specific population. Simulation studies are conducted to evaluate the proposed method. RESULTS: The accuracy and precision of IBIS could reach a desired level in terms of estimation performance. In regard to subgroup identification and population enrichment, the proposed IBIS has superior and robust characteristics compared with traditional methods. An example of how to obtain design parameters for an adaptive enrichment design under the IBIS framework is also provided. CONCLUSIONS: IBIS has the potential to be a useful tool for biomarker-based subgroup identification and population enrichment in clinical trials of targeted combination therapy.

Epidemiological and genomic analysis of dengue cases in Guangzhou, China, from 2010 to 2019.

With a long epidemic history and a large number of dengue cases, Guangzhou is a key city for controlling dengue in China. The demographic information regarding dengue cases, and the genomic characteristics of the envelope gene of dengue viruses, as well as the associations between these factors were investigated from 2010 to 2019, to improve the understanding of the epidemiology of dengue in Guangzhou. Demographic data on 44,385 dengue cases reported to the Notifiable Infectious Disease Report System were analyzed using IBM SPSS Statistics v. 20. Dengue virus isolates from patient sera were sequenced, and phylogenetic trees were constructed using PhyML 3.1. There was no statistical difference in the risk of dengue infection between males and females. Unlike other areas in which dengue is endemic, the infection risk in Guangzhou increased with age. Surveillance identified four serotypes responsible for dengue infections in Guangzhou. Serotype 1 remained prevalent for most of the study period, whereas serotypes 3 and 4 were prevalent in 2012 and 2010, respectively. Different serotypes underwent genotype and sublineage shifts. The epidemiological characteristics and phylogeny of dengue in Guangzhou suggested that although it has circulated in Guangzhou for decades, it has not been endemic in Guangzhou. Meanwhile, shifts in genotypes, rather than in serotypes, might have caused dengue epidemics in Guangzhou.

Recent progress of theoretical studies on electro- and photo-chemical conversion of CO2 with single-atom catalysts.

The CO2 reduction reaction (CO2RR) into chemical products is a promising and efficient way to combat the global warming issue and greenhouse effect. The viability of the CO2RR critically rests with finding highly active and selective catalysts that can accomplish the desired chemical transformation. Single-atom catalysts (SACs) are ideal in fulfilling this goal due to the well-defined active sites and support-tunable electronic structure, and exhibit enhanced activity and high selectivity for the CO2RR. In this review, we present the recent progress of quantum-theoretical studies on electro- and photo-chemical conversion of CO2 with SACs and frameworks. Various calculated products of CO2RR with SACs have been discussed, including CO, acids, alcohols, hydrocarbons and other organics. Meanwhile, the critical challenges and the pathway towards improving the efficiency of the CO2RR have also been discussed.

Shotgun-2: A Bayesian phase I/II basket trial design to identify indication-specific optimal biological doses.

For novel molecularly targeted agents and immunotherapies, the objective of dose-finding is often to identify the optimal biological dose, rather than the maximum tolerated dose. However, optimal biological doses may not be the same for different indications, challenging the traditional dose-finding framework. Therefore, we proposed a Bayesian phase I/II basket trial design, named "shotgun-2," to identify indication-specific optimal biological doses. A dose-escalation part is conducted in stage I to identify the maximum tolerated dose and admissible dose sets. In stage II, dose optimization is performed incorporating both toxicity and efficacy for each indication. Simulation studies under both fixed and random scenarios show that, compared with the traditional "phase I + cohort expansion" design, the shotgun-2 design is robust and can improve the probability of correctly selecting the optimal biological doses. Furthermore, this study provides a useful tool for identifying indication-specific optimal biological doses and accelerating drug development.

Elastic priors to dynamically borrow information from historical data in clinical trials.

Use of historical data and real-world evidence holds great potential to improve the efficiency of clinical trials. One major challenge is to effectively borrow information from historical data while maintaining a reasonable type I error and minimal bias. We propose the elastic prior approach to address this challenge. Unlike existing approaches, this approach proactively controls the behavior of information borrowing and type I errors by incorporating a well-known concept of clinically significant difference through an elastic function, defined as a monotonic function of a congruence measure between historical data and trial data. The elastic function is constructed to satisfy a set of prespecified criteria such that the resulting prior will strongly borrow information when historical and trial data are congruent, but refrain from information borrowing when historical and trial data are incongruent. The elastic prior approach has a desirable property of being information borrowing consistent, that is, asymptotically controls type I error at the nominal value, no matter that historical data are congruent or not to the trial data. Our simulation study that evaluates the finite sample characteristic confirms that, compared to existing methods, the elastic prior has better type I error control and yields competitive or higher power. The proposed approach is applicable to binary, continuous, and survival endpoints.

Metabolism-associated molecular classification of gastric adenocarcinoma.

Most gastric cancers (GC) are adenocarcinomas, whereas GC is a highly heterogeneous disease due to its molecular heterogeneity. However, traditional morphology-based classification systems, including the WHO classification and Lauren's classification, have limited utility in guiding clinical treatment. We performed nonnegative matrix factorization (NMF) clustering based on 2752 metabolism-associated genes. We characterized each of the subclasses from multiple angles, including subclass-associated metabolism signatures, immune cell infiltration, clinic10al characteristics, drug sensitivity, and pathway enrichment. As a result, four subtypes were identified: immune suppressed, metabolic, mesenchymal/immune exhausted and hypermutated. The subtypes exhibited significant prognostic differences, which suggests that the metabolism-related classification has clinical significance. Metabolic and hypermutated subtypes have better overall survival, and the hypermutated subtype is likely to be sensitive to anti-PD-1 immunotherapy. In addition, our work showed a strong connection with previously established classifications, especially Lei's subtype, to which we provided an interpretation based on the immune cell infiltration perspective, deepening the understanding of GC heterogeneity. Finally, a 120-gene classifier was generated to determine the GC classification, and a 10-gene prognostic model was developed for survival time prediction.