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Alternative splicing has been shown to participate in tumor progression, including hepatocellular carcinoma. The poor prognosis of patients with HCC calls for molecular classification and biomarker identification to facilitate precision medicine. We performed ssGSEA analysis to quantify the pathway activity of RNA splicing in three HCC cohorts. Kaplan–Meier and Cox methods were used for survival analysis. GO and GSEA were performed to analyze pathway enrichment. We confirmed that RNA splicing is significantly correlated with prognosis, and identified an alternative splicing-associated protein LUC7L3 as a potential HCC prognostic biomarker. Further bioinformatics analysis revealed that high LUC7L3 expression indicated a more progressive HCC subtype and worse clinical features. Cell proliferation-related pathways were enriched in HCC patients with high LUC7L3 expression. Consistently, we proved that LUC7L3 knockdown could significantly inhibit cell proliferation and suppress the activation of associated signaling pathways in vitro. In this research, the relevance between RNA splicing and HCC patient prognosis was outlined. Our newly identified biomarker LUC7L3 could provide stratification for patient survival and recurrence risk, facilitating early medical intervention before recurrence or disease progression.

Abstract Background Intrahepatic cholangiocarcinoma (iCCA) is a highly heterogeneous and lethal hepatobiliary tumor with few therapeutic strategies. The metabolic reprogramming of tumor cells plays an essential role in the development of tumors, while the metabolic molecular classification of iCCA is largely unknown. Here, we performed an integrated multiomics analysis and metabolic classification to depict differences in metabolic characteristics of iCCA patients, hoping to provide a novel perspective to understand and treat iCCA. Methods We performed integrated multiomics analysis in 116 iCCA samples, including whole‐exome sequencing, bulk RNA‐sequencing and proteome analysis. Based on the non‐negative matrix factorization method and the protein abundance of metabolic genes in human genome‐scale metabolic models, the metabolic subtype of iCCA was determined. Survival and prognostic gene analyses were used to compare overall survival (OS) differences between metabolic subtypes. Cell proliferation analysis, 5‐ethynyl‐2'‐deoxyuridine (EdU) assay, colony formation assay, RNA‐sequencing and Western blotting were performed to investigate the molecular mechanisms of diacylglycerol kinase α (DGKA) in iCCA cells. Results Three metabolic subtypes (S1‐S3) with subtype‐specific biomarkers of iCCA were identified. These metabolic subtypes presented with distinct prognoses, metabolic features, immune microenvironments, and genetic alterations. The S2 subtype with the worst survival showed the activation of some special metabolic processes, immune‐suppressed microenvironment and Kirsten rat sarcoma viral oncogene homolog (KRAS)/AT‐rich interactive domain 1A (ARID1A) mutations. Among the S2 subtype‐specific upregulated proteins, DGKA was further identified as a potential drug target for iCCA, which promoted cell proliferation by enhancing phosphatidic acid (PA) metabolism and activating mitogen‐activated protein kinase (MAPK) signaling. Conclusion Via multiomics analyses, we identified three metabolic subtypes of iCCA, revealing that the S2 subtype exhibited the poorest survival outcomes. We further identified DGKA as a potential target for the S2 subtype.

Abstract Introduction The COVID-19 epidemic control and prevention strategies affected people’s sexual activities and behaviors. Little was known about long-term effects of COVID-19 prevention and control strategies on sexual behaviors among men who have sex with men (MSM). This study aimed to examine changes in risky sexual behaviors of MSM before and after the local epidemic. Methods An online survey was conducted nationwide from June 1 to June 10, 2022. MSM aged 16 years and above, residing in China were recruited through convenience sampling. A generalized estimating equation model with modified Poisson regression was used to analyze changes in multiple sexual partners, unprotected sex, mobility for sexual activity, and recreational substance use before and after the local epidemic. Results Compared to the pre-pandemic (36.5%), the prevalence of multiple sexual partners (11.5%) significantly decreased during the local epidemic and then increased after the local epidemic (25.2%) but remained lower than pre-pandemic, as did the prevalence of unprotected sex (31.1%, 19.4%, and 26.1%), mobility for sexual activity (7.5%, 2.8%, and 4.1%) and recreational substance use (47.7%, 27.2%, and 39.5%). Compared to the pre-pandemic, higher declines in the prevalence of risky sexual behaviors during the local epidemic existed among MSM living without a regular partner (44% decrease in unprotected sex and 46% in recreational substance use), with a bachelor’s degree and above (70% decrease in multiple sex partners, 39% in unprotected sex, 67% in mobility for sexual activity and 44% in recreational substance use), higher incomes (70% decrease in multiple sex partners), self-identified gay or bisexual/unsure (38-71%), and HIV infection (49-83% decrease respectively in these four indicators). After the local epidemic, the declines in the above indicators compared to the pre-pandemic were correspondingly. And higher declines existed among MSM living without a regular partner (8% decrease in unprotected sex and 13% in recreational substance use), with a bachelor’s degree and above (33% decrease in multiple sex partners), higher incomes (55% decrease in mobility for sexual activity), self-identified gay (51% decrease in mobility for sexual activity), and HIV infection (32%, 68%, 24% decrease respectively in unprotected sex, mobility for sexual activity and recreational substance use). Conclusions Risky sexual behaviors reduced considerably during the local epidemic, then seemed rebounded after the outbreak but wouldn’t return to pre-pandemic levels. More attention should be paid to vulnerable people with lower socio-economic status, HIV-positive, and sexual minorities for sustained HIV and COVID-19 prevention.

Abstract Background To estimate crude mortality, excess mortality, and standardized mortality rates (SMR) among people living with HIV (PLHIV) initiating highly active antiretroviral therapy (HAART) in Luzhou, China 2006–2020, and assess associated factors. Methods PLHIV initiating HAART in the HIV/AIDS Comprehensive Response Information Management System (CRIMS) in Luzhou, China 2006–2020 were included in the retrospective cohort study. The crude mortality, excess mortality, and SMR were estimated. Multivariable Poisson regression model was used for analyzing risk factors associated with excess mortality rates. Results The median age among 11,468 PLHIV initiating HAART was 54.5 years (IQR:43.1–65.2). The excess mortality rate decreased from 1.8 deaths/100 person-years (95% confidence interval [CI]:1.4–2.4) in 2006–2011 to 0.8 deaths/100 person-years (95%CI:0.7–0.9) in 2016–2020. SMR decreased from 5.4 deaths/100 person-years (95%CI:4.3–6.8) to 1.7 deaths/100 person-years (95%CI:1.5–1.8). Males had greater excess mortality with the eHR of 1.6 (95%CI:1.2–2.1) than females. PLHIV with CD4 counts ≥ 500 cells/μL had the eHR of 0.3 (95%CI:0.2–0.5) in comparison to those with CD4 counts

BackgroundOsteosarcoma is the most common primary malignant bone tumor. The existing treatment regimens remained essentially unchanged over the past 30 years; hence the prognosis has plateaued at a poor level. Precise and personalized therapy is yet to be exploited.MethodsOne discovery cohort (n=98) and two validation cohorts (n=53 & n=48) were collected from public data sources. We performed a non-negative matrix factorization (NMF) method on the discovery cohort to stratify osteosarcoma. Survival analysis and transcriptomic profiling characterized each subtype. Then, a drug target was screened based on subtypes’ features and hazard ratios. We also used specific siRNAs and added a cholesterol pathway inhibitor to osteosarcoma cell lines (U2OS and Saos-2) to verify the target. Moreover, PermFIT and ProMS, two support vector machine (SVM) tools, and the least absolute shrinkage and selection operator (LASSO) method, were employed to establish predictive models.ResultsWe herein divided osteosarcoma patients into four subtypes (S-I ~ S-IV). Patients of S- I were found probable to live longer. S-II was characterized by the highest immune infiltration. Cancer cells proliferated most in S-III. Notably, S-IV held the most unfavorable outcome and active cholesterol metabolism. SQLE, a rate-limiting enzyme for cholesterol biosynthesis, was identified as a potential drug target for S-IV patients. This finding was further validated in two external independent osteosarcoma cohorts. The function of SQLE to promote proliferation and migration was confirmed by cell phenotypic assays after the specific gene knockdown or addition of terbinafine, an inhibitor of SQLE. We further employed two machine learning tools based on SVM algorithms to develop a subtype diagnostic model and used the LASSO method to establish a 4-gene model for predicting prognosis. These two models were also verified in a validation cohort.ConclusionThe molecular classification enhanced our understanding of osteosarcoma; the novel predicting models served as robust prognostic biomarkers; the therapeutic target SQLE opened a new way for treatment. Our results served as valuable hints for future biological studies and clinical trials of osteosarcoma.

ObjectiveReducing the prevalence of treatment failure among people living with HIV (PLHIV) on highly active antiretroviral therapy (HAART) is crucial for improving individual health and reducing disease burden. This study aimed to assess existing evidence on treatment failure and its associated factors among PLHIV in mainland China.MethodsWe conducted a comprehensive search of PubMed, Web of Science, Cochrane Library, WanFang, China National Knowledge Infrastructure, and SinoMed databases. Relevant studies on treatment failure among PLHIV in mainland China until September 2022 were searched, including cross-sectional, case-control, and cohort studies. The primary outcome was treatment failure, and secondary outcomes were the potential influencing factors of treatment failure. We performed a meta-analysis to pool each outcome of interest, including meta-regression, subgroup, publication bias, and sensitivity analyses.ResultsA total of 81 studies were deemed eligible and included in the final meta-analysis. The pooled treatment failure prevalence among PLHIV in mainland China was 14.40% (95% confidence interval [CI]:12.30-16.63), of which the virological and immunological failure prevalence was 10.53% (95%CI:8.51-12.74) and 18.75% (95%CI:15.44-22.06), respectively. The treatment failure prevalence before and after 2016 was 18.96% (95%CI:13.84-24.67) and 13.19% (95%CI:10.91-15.64). Factors associated with treatment failure included good treatment adherence (odds ratio [OR] = 0.36, 95%CI:0.26-0.51), baseline CD4 counts>200 cells/μL (OR = 0.39, 95%CI:0.21-0.75), HAART regimens containing Tenofovir Disoproxil Fumarate (TDF) (OR = 0.70, 95%CI:0.54-0.92), WHO clinical stage III/IV (OR = 2.02, 95%CI:1.14-3.59) and age≥40 years (OR = 1.56, 95%CI:1.23-1.97).ConclusionThe prevalence of treatment failure among PLHIV receiving HAART in mainland China was low and tended to decline. Poor adherence, low baseline CD4 count, HAART regimens without TDF, advanced clinical stage, and old age were contributing factors for treatment failure. Relevant intervention programs are needed with increasing treatment adherence through behavioral intervention or precise intervention targeting older adults.

Abstract Background One striking feature of the large KRAB domain-containing zinc finger protein (KZFP) family is its rapid evolution, leading to hundreds of member genes with various origination time in a certain mammalian genome. However, a comprehensive genome-wide and across-taxa analysis of the structural and expressional features of KZFPs with different origination time is lacking. This type of analysis will provide valuable clues about the functional characteristics of this special family. Results In this study, we found several conserved paradoxical phenomena about this issue. 1) Ordinary young domains/proteins tend to be disordered, but most of KRAB domains are completely structured in 64 representative species across the superclass of Sarcopterygii and most of KZFPs are also highly structured, indicating their rigid and unique structural and functional characteristics; as exceptions, old-zinc-finger-containing KZFPs have relatively disordered KRAB domains and linker regions, contributing to diverse interacting partners and functions. 2) In general, young or highly structured proteins tend to be spatiotemporal specific and have low abundance. However, by integrated analysis of 29 RNA-seq datasets, including 725 samples across early embryonic development, embryonic stem cell differentiation, embryonic and adult organs, tissues in 7 mammals, we found that KZFPs tend to express ubiquitously with medium abundance regardless of evolutionary age and structural disorder degree, indicating the wide functional requirements of KZFPs in various states. 3) Clustering and correlation analysis reveal that there are differential expression patterns across different spatiotemporal states, suggesting the specific-high-expression KZFPs may play important roles in the corresponding states. In particular, part of young-zinc-finger-containing KZFPs are highly expressed in early embryonic development and ESCs differentiation into endoderm or mesoderm. Co-expression analysis revealed that young-zinc-finger-containing KZFPs are significantly enriched in five co-expression modules. Among them, one module, including 13 young-zinc-finger-containing KZFPs, showed an ‘early-high and late-low’ expression pattern. Further functional analysis revealed that they may function in early embryonic development and ESC differentiation via participating in cell cycle related processes. Conclusions This study shows the conserved and special structural, expressional features of KZFPs, providing new clues about their functional characteristics and potential causes of their rapid evolution.

Recent years have witnessed increasing research interest in human behavior recognition as it provides attractive applications in various sensing scenarios. Among these encouraging implementations, device-free behavior recognition based on WiFi channel state information (CSI) has attracted significant attention due to the popularity of WiFi devices and abundant channel characteristics from CSI. Meanwhile, the CSI signal provides us with additional benefits because it can propagate through a wall. This through-the-wall and device-free scheme not only enables us to identify specific human actions but also to infer person activities by collecting the data from different rooms. This paper presents a survey on the state-of-art progresses in device-free through-the-wall human behavior recognition based on CSI. Specifically, this paper first introduces the basic concept of CSI and describes the signal variation caused by human behavior. Then, it illustrates that different human behaviors can cause signal transformation. Therefore, the unique map relationship between action and signal variation can be leveraged to recognize human behavior. Next, it provides the general architecture of through-the-wall behavior recognition and highlights its core characteristic. It investigates the state-of-art applications in various scenarios and analyzes specific design schemes and implementations. Afterward, it discusses the various across wall applications and makes a detailed comparison between non-through-the-wall and through-the-wall applications. Meanwhile, it analyzes many factors that affect recognition accuracy and emphasizes performance differences under across wall scenarios. Finally, this paper concludes by summarizing the issues and challenges faced and providing insights into the possible solution and future research trend.

Recently, device-free human behavior recognition has become a hot research topic and has achieved significant progress in the field of ubiquitous computing. Among various implementation, behavior recognition based on WiFi CSI (channel state information) has drawn wide attention due to its major advantages. This paper investigates more than 100 latest CSI based behavior recognition applications within the last 6 years and presents a comprehensive survey from every aspect of human behavior recognition. Firstly, this paper reviews general behavior recognition applications using the WiFi signal and presents the basic concept of CSI and the fundamental principle of CSI-based behavior recognition. This paper analyzes the key components and core characteristics of the system architecture of human behavior recognition using CSI. Afterward, we divide the sensing procedures into many steps and summarize the typical studies from these steps, including base signal selection, signal preprocessing, and identification approaches. Next, based on the recognition technique, we classify the applications into three groups, including pattern-based, model-based, and deep learning-based approach. In every group, we categorize the state-of-the-art applications into three groups, including coarse-grained specific behavior recognition, fine-grained specific behavior recognition, and activity inference. It elaborates the typical behavior recognition applications from five aspects, including experimental equipment, experimental scenario, behavior, classifier, and system performance. Then, this paper presents comprehensive discussions about representative applications from the implementation view and outlines the major consideration when developing a recognition system. Finally, this article concludes by analyzing the open issues of CSI-based behavior recognition applications and pointing out future research directions.

With the rapid progress of the Internet of Things (IoT) technology, human behavior recognition has become an important research topic in the field of ubiquitous computing and has obtained quite a number of research achievements. Accurate human behavior recognition can enhance the quality of human-computer interaction and facilitate the development of various sensing applications. With the popularity of smartphones and the improved performance of sensors such as speakers and microphones built in smartphones, the behavior recognition technique based on ultrasound signal of the smartphone has gained more attention and achieved several research results. In this paper, we first review the common behavior recognition techniques including light, video, sound, and frequency radio and outline their main characteristics. Then, we introduce the fundamental principle of human behavior recognition based on ultrasound signals. Specifically, these systems treat speakers and microphones embedded in smartphones as the transceiver and leverage the received signal changes caused by human movement including phase differences, frequency shift, and time of flight (ToF) to recognize human behavior. Next, we investigate the state-of-the-art studies and applications and analyze the signal processing techniques such as data collection, signal preprocessing, feature description, and behavior recognition approach. Afterward, according to the purpose of these applications, we classify them into five groups and compare them in detail including hand gesture recognition, activity recognition, hand trajectory tracking, vital sign monitoring, and lip reading. Finally, we conclude by discussing the limitations, challenges, and open issues involved in behavior recognition based on ultrasound signal of smartphone.

With the rapid development of Internet of Things, hand gesture recognition has drawn wide attention in the field of ubiquitous computing because it provides us with simple and natural human-computer interaction mode. Among these various implementations, hand gesture recognition using ultrasonic signals of smartphone has become a hot research topic due to its various advantages. In this paper, we consider the smartphone as an active sonar sensing system to identify hand movements. Specifically, the speakers emit ultrasonic signal and the microphone on the same phone receives the changed echo affected by hand movements. This paper investigates the state-of-the-art hand gesture applications and presents a comprehensive survey on the characteristics of studies using the active sonar sensing system. Firstly, we review the existing research of hand gesture recognition based on acoustic signals. After that, we introduce the characteristics of ultrasonic signal and describe the fundamental principle of hand gesture recognition. Then, we focus on the typical methods used in these studies and present a detailed analysis on signal generation, feature extraction, preprocessing, and recognition methods. Next, we investigate the state-of-the-art ultrasonic-based applications of hand gesture recognition using smartphone and analyze them in detail from dynamic gesture recognition and hand tracking. Afterwards, we make a discussion about these systems from signal acquisition, signal processing, and performance evaluation to obtain some insight into development of the ultrasonic hand gesture recognition system. Finally, we conclude by discussing the challenges, insight, and open issues involved in hand gesture recognition based on ultrasonic signal of the smartphone.