Interaction between VPS13A and the XK scramblase is important for VPS13A function in humans.

VPS13 family proteins form conduits between the membranes of different organelles through which lipids are transferred. In humans, there are four VPS13 paralogs, and mutations in the genes encoding each of them are associated with different inherited disorders. VPS13 proteins contain multiple conserved domains. The Vps13 adaptor-binding (VAB) domain binds to adaptor proteins that recruit VPS13 to specific membrane contact sites. This work demonstrates the importance of a different domain in VPS13A function. The pleckstrin homology (PH) domain at the C-terminal region of VPS13A is required to form a complex with the XK scramblase and for the co-localization of VPS13A with XK within the cell. Alphafold modeling was used to predict an interaction surface between VPS13A and XK. Mutations in this region disrupt both complex formation and co-localization of the two proteins. Mutant VPS13A alleles found in patients with VPS13A disease truncate the PH domain. The phenotypic similarities between VPS13A disease and McLeod syndrome caused by mutations in VPS13A and XK, respectively, argue that loss of the VPS13A-XK complex is the basis of both diseases.

The role of Nurr1-miR-30e-5p-NLRP3 axis in inflammation-mediated neurodegeneration: insights from mouse models and patients' studies in Parkinson's disease.

Nuclear receptor related-1 (Nurr1), a ligand-activated transcription factor, is considered a potential susceptibility gene for Parkinson's disease (PD), and has been demonstrated to possess protective effects against inflammation-induced neuronal damage. Despite the evidence showing decreased NURR1 level and increased pro-inflammatory cytokines in cell and animal models as well as in PD patients' peripheral blood mononuclear cells (PBMCs), the underlying mechanism remains elusive. In this study, we investigated the molecular mechanism of Nurr1 in PD-related inflammation. Through the miRNA-sequencing and verification in PBMCs from a cohort of 450 individuals, we identified a significant change of a Nurr1-dependent miRNA miR-30e-5p in PD patients compared to healthy controls (HC). Additionally, PD patients exhibited an elevated plasma interleukin-1ß (IL-1ß) level and increased nucleotide-binding domain-like receptor protein 3 (NLRP3) expression in PBMCs compared to HC. Statistical analyses revealed significant correlations among NURR1, miR-30e-5p, and NLRP3 levels in the PBMCs of PD patients. To further explore the involvement of Nurr1-miR-30e-5p-NLRP3 axis in the inflammation-mediated PD pathology, we developed a mouse model (Nurr1flox+/Cd11b-cre+, Nurr1cKO) conditionally knocking out Nurr1 in Cd11b-expressing cells. Our investigations in Nurr1cKO mice unveiled significant dopaminergic neurodegeneration following lipopolysaccharide-induced inflammation. Remarkably, Nurr1 deficiency triggered microglial activation and activated NLRP3 inflammasome, resulting in increased IL-1ß secretion. Coincidently, we found that miR-30e-5p level was significantly decreased in the PBMCs and primary microglia of Nurr1cKO mice compared to the controls. Furthermore, our in vitro experiments demonstrated that miR-30e-5p specifically targeted NLRP3. In Nurr1-knockdown microglia, NLRP3 expression was upregulated via miR-30e-5p. In summary, our findings highlight the involvement of Nurr1-miR-30e-5p-NLRP3 axis in the inflammation-mediated neurodegeneration in PD, the results of which may offer promising prospects for developing PD biomarkers and targeted therapeutic interventions.

Identification of atrial fibrillation phenotypes at low risk of stroke in patients with CHA2DS2-VASc ≥2: Insight from the China-AF study.

OBJECTIVE: Patients with atrial fibrillation (AF) are highly heterogeneous, and current risk stratification scores are only modestly good at predicting an individual's stroke risk. We aim to identify distinct AF clinical phenotypes with cluster analysis to optimize stroke prevention practices. METHODS: From the prospective Chinese Atrial Fibrillation Registry cohort study, we included 4337 AF patients with CHA2 DS2 -VASc≥2 for males and 3 for females who were not treated with oral anticoagulation. We randomly split the patients into derivation and validation sets by a ratio of 7:3. In the derivation set, we used outcome-driven patient clustering with metric learning to group patients into clusters with different risk levels of ischemic stroke and systemic embolism, and identify clusters of patients with low risks. Then we tested the results in the validation set, using the clustering rules generated from the derivation set. Finally, the survival decision tree was applied as a sensitivity analysis to confirm the results. RESULTS: Up to the follow-up of 1 year, 140 thromboembolic events (ischemic stroke or systemic embolism) occurred. After supervised metric learning from six variables involved in CHA2 DS2 -VASc scheme, we identified a cluster of patients (255/3035, 8.4%) at an annual thromboembolism risk of 0.8% in the derivation set. None of the patients in the low-risk cluster had prior thromboembolism, heart failure, diabetes, or age older than 70 years. After applying the regularities from metric learning on the validation set, we also identified a cluster of patients (137/1302, 10.5%) with an incident thromboembolism rate of 0.7%. Sensitivity analysis based on the survival decision tree approach selected a subgroup of patients with the same phenotypes as the metric-learning algorithm. CONCLUSIONS: Cluster analysis identified a distinct clinical phenotype at low risk of stroke among high-risk [CHA2 DS2 -VASc≥2 (3 for females)] patients with AF. The use of the novel analytic approach has the potential to prevent a subset of AF patients from unnecessary anticoagulation and avoid the associated risk of major bleeding.

Mis-localization of endogenous TDP-43 leads to ALS-like early-stage metabolic dysfunction and progressive motor deficits.

BACKGROUND: The key pathological signature of ALS/ FTLD is the mis-localization of endogenous TDP-43 from the nucleus to the cytoplasm. However, TDP-43 gain of function in the cytoplasm is still poorly understood since TDP-43 animal models recapitulating mis-localization of endogenous TDP-43 from the nucleus to the cytoplasm are missing. METHODS: CRISPR/Cas9 technology was used to generate a zebrafish line (called CytoTDP), that mis-locates endogenous TDP-43 from the nucleus to the cytoplasm. Phenotypic characterization of motor neurons and the neuromuscular junction was performed by immunostaining, microglia were immunohistochemically localized by whole-mount tissue clearing and muscle ultrastructure was analyzed by scanning electron microscopy. Behavior was investigated by video tracking and quantitative analysis of swimming parameters. RNA sequencing was used to identify mis-regulated pathways with validation by molecular analysis. RESULTS: CytoTDP fish have early larval phenotypes resembling clinical features of ALS such as progressive motor defects, neurodegeneration and muscle atrophy. Taking advantage of zebrafish's embryonic development that solely relys on yolk usage until 5 days post fertilization, we demonstrated that microglia proliferation and activation in the hypothalamus is independent from food intake. By comparing CytoTDP to a previously generated TDP-43 knockout line, transcriptomic analyses revealed that mis-localization of endogenous TDP-43, rather than TDP-43 nuclear loss of function, leads to early onset metabolic dysfunction. CONCLUSIONS: The new TDP-43 model mimics the ALS/FTLD hallmark of progressive motor dysfunction. Our results suggest that functional deficits of the hypothalamus, the metabolic regulatory center, might be the primary cause of weight loss in ALS patients. Cytoplasmic gain of function of endogenous TDP-43 leads to metabolic dysfunction in vivo that are reminiscent of early ALS clinical non-motor metabolic alterations. Thus, the CytoTDP zebrafish model offers a unique opportunity to identify mis-regulated targets for therapeutic intervention early in disease progression.

Towards toxic and narcotic medication detection with rotated object detectors.

Background: Recent years have witnessed the advancement of deep learning vision technologies and applications in the medical industry. Intelligent devices for specific medication management could alleviate workload of medical staff by providing assistance services to identify drug specifications and locations. Methods: In this work, object detectors based on the you only look once (YOLO) algorithm are tailored for toxic and narcotic medication detection tasks in which there are always numerous of arbitrarily oriented small bottles. Specifically, we propose a flexible annotation process that defines a rotated bounding box with a degree ranging from 0° to 90° without worry about the long-short edges. Moreover, a mask-mapping-based non-maximum suppression method has been leveraged to accelerate the post-processing speed and achieve a feasible and efficient medication detector that identifies arbitrarily oriented bounding boxes. Results: Extensive experiments have demonstrated that rotated YOLO detectors are highly suitable for identifying densely arranged drugs. Six thousand synthetic data and 523 hospital collected images have been taken for training of the network. The mean average precision of the proposed network reaches 0.811 with an inference time of less than 300 ms. Conclusions: This study provides an accurate and fast drug detection solution for the management of special medications. The proposed rotated YOLO detector outperforms its YOLO counterpart in terms of precision.

Different degrees of nodes behind obsessive-compulsive symptoms of schizophrenia.

Obsessive-compulsive symptoms are frequently observed in various psychiatric disorders, including obsessive-compulsive disorder, schizophrenia, depression, and anxiety. However, the underlying anatomical basis of these symptoms remains unclear. In this study, we aimed to investigate the mechanism of schizophrenia with obsessive-compulsive symptoms by using diffusion tensor imaging (DTI)-based structural brain connectivity analysis to assess the network differences between patients with obsessive-compulsive disorder (OCD), patients with schizophrenia showing obsessive-compulsive symptoms (SCH), schizophrenia patients with obsessive-compulsive symptoms due to clozapine (LDP), and healthy controls (CN). We included 21 patients with OCD, 20 patients with SCH, 12 patients with LDP, and 25 CN. All subjects underwent MRI scanning, and structural brain connections were estimated using diffusion tensor imaging for further analysis of brain connectivity. The topology and efficiency of the network and the characteristics of various brain regions were investigated. We assessed baseline YALE-BROWN OBSESSIVE COMPULSIVE SCALE (Y-BOCS), Positive and Negative Syndrome Scale (PANSS), and 24-item Hamilton Depression Scale (HAMD-24) scores. Our results showed significant differences among the SCH, OCD, and CN groups (p < 0.05) in the MRI-measured degree of the following nodes: the superior orbitofrontal gyrus (25Frontal_Med_Orb_L), lingual gyrus (47Lingual_L), postcentral gyrus (58Postcentral_R), and inferior temporal gyrus (90Temporal_Inf_R). Additionally, we found significant differences in the degree of the brain regions 02Precentral_R, 47Lingual_L, 58Postcentral_R, and 90Temporal_Inf_R between the CN, OCD, SCH, and LDP groups (p < 0.05). These findings suggest that alterations in the degree of nodes might be the mechanism behind obsessive-compulsive symptoms in schizophrenia.

CircRNA SOD2 motivates non-small cell lungs cancer advancement with EMT via acting as microRNA-2355-5p's competing endogenous RNA to mediate calmodulin regulated spectrin associated proteins-2.

Circular RNAs (circRNAs) are closely linked with human cancer development such as non-small-cell lung cancer (NSCLC). However, the characteristics and specific functions of most circRNAs in NSCLC remained unknown. Previous studies have suggested that circRNA SOD2 (CircSOD2) expression was upregulated in a number of cancers. This study aimed to explore the functions of circSOD2 in NSCLC advancement with epithelial-mesenchymal transition (EMT). Expression profile analysis of circSOD2, miR-2355-5p, and calmodulin-regulated spectrin-associated protein 2 (CAMSAP2) was detected by real-time quantitative PCR (RT-qPCR). Transwell assay, cell migration assay, CCK8, ELISA, RIP assay, RNA pull-down assay, and Western blot analysis were performed to evaluate the functions of circSOD2, miR-2355-5p, and CAMSAP2. We found elevated expression of circSOD2 and CAMSAP2 while reduced expression of miR-2355-5p in NSCLC tumor tissues. Silencing or overexpression of CircSOD2 resulted in increased or decreased expression of miR-2355-5p, respectively. Mechanically, we showed that silencing of CircSOD2 and overexpression of miR-2355-5p resulted in the reduced rate of NSCLC cell proliferation. Inhibition of miR-2355-5p reversed the changes induced via silencing of CircSOD2. MiR-2355-5p binds to the CircSOD2 promoter and triggered its stimulation, which further activated circSOD2 expression. CircSOD2 suppression impaired lung cancer cell growth, cell migration, prohibited cell cycle progression, and in vivo tumor growth by targeting miR-2355-5p expression in NSCLC tissues. Meanwhile, increased expression of CAMSAP2 reversed the changes stimulated by the elevated level of miR-2355-5p in NSCLC progression. This innovative signaling axis CircSOD2/miR-2355-5p/CAMSAP2 illustrated the new horizon to investigate NSCLC tumorigenesis and provided new prognosis and treatment of NSCLC.

Autocrined leptin promotes proliferation of non-small cell lung cancer (NSCLC) via PI3K/AKT and p53 pathways.

BACKGROUND: Circulating leptin can directly act on tumor cells. However, a recent meta-analysis showed that plasma leptin concentration had no significant effect on the survival of lung cancer patients. So does Leptin have an effect on lung cancer? Or there may be other factors that influence the effect. METHODS: Genome sequencing database Oncomine was searched to learn the differential expression of leptin between tumors and normal lungs. Fresh tumor specimens and paired normal lung tissue from six lung adenocarcinoma patients were collected, and validate the expression level of leptin. Clinicopathological information and tumor slices from 60 non-small cell lung cancer (NSCLC) patients were analyzed to evaluate the prognostic value of autocrined leptin. Whole genome sequencing data from the cancer genome atlas (TCGA) was analyzed to predict the underlying mechanism of leptin regulating tumor proliferation. Finally, these findings were confirmed by using cell lines H1299, A549, H460, and H322 to explore the promoting effect and mechanism of leptin on cell proliferation in vitro. RESULTS: Five datasets in Oncomine reported the expression of the LEP gene in NSCLC, and 4 datasets showed that leptin was up-regulated in tumors compared with normal lungs. Leptin was also overexpressed in 5 out of 6 clinical lung adenocarcinoma specimens. The analysis of the 60 NSCLC patients revealed that autocrined leptin could serve as an auxiliary prognostic factor, and a higher expression of leptin indicated a higher survival risk. Gene set enrichment analysis (GSEA) showed that the PI3K/AKT/mTOR signaling pathway was positively enriched when the LEP gene was highly expressed, while the P53 signaling pathway was negatively enriched. Leptin promoted cell cycle and clone formation in H1299 and A549 cells, up-regulation or down-regulation of leptin in these two cell lines led to enhanced or declined proliferation. Finally, it was confirmed that the PI3K/AKT/mTOR signaling pathway was positively regulated by leptin expression, while the P53 signaling pathway was negatively regulated. CONCLUSIONS: Autocrined leptin was observed in majority of NSCLC tissue, which could serve as an auxiliary prognostic factor for NSCLC patients. Autocrined leptin had a promoting effect on the proliferation of NSCLC cells, which probably positively regulating the PI3K/AKT/mTOR signaling pathway and negatively regulate the P53 signaling pathway.

Role of efficient testing and contact tracing in mitigating the COVID-19 pandemic: a network modelling study.

OBJECTIVES: This study quantified how the efficiency of testing and contact tracing impacts the spread of COVID-19. The average time interval between infection and quarantine, whether asymptomatic cases are tested or not, and initial delays to beginning a testing and tracing programme were investigated. SETTING: We developed a novel individual-level network model, called CoTECT (Testing Efficiency and Contact Tracing model for COVID-19), using key parameters from recent studies to quantify the impacts of testing and tracing efficiency. The model distinguishes infection from confirmation by integrating a 'T' compartment, which represents infections confirmed by testing and quarantine. The compartments of presymptomatic (E), asymptomatic (I), symptomatic (Is), and death with (F) or without (f) test confirmation were also included in the model. Three scenarios were evaluated in a closed population of 3000 individuals to mimic community-level dynamics. Real-world data from four Nordic countries were also analysed. PRIMARY AND SECONDARY OUTCOME MEASURES: Simulation result: total/peak daily infections and confirmed cases, total deaths (confirmed/unconfirmed by testing), fatalities and the case fatality rate. Real-world analysis: confirmed cases and deaths per million people. RESULTS: (1) Shortening the duration between Is and T from 12 to 4 days reduces infections by 85.2% and deaths by 88.8%. (2) Testing and tracing regardless of symptoms reduce infections by 35.7% and deaths by 46.2% compared with testing only symptomatic cases. (3) Reducing the delay to implementing a testing and tracing programme from 50 to 10 days reduces infections by 35.2% and deaths by 44.6%. These results were robust to sensitivity analysis. An analysis of real-world data showed that tests per case early in the pandemic are critical for reducing confirmed cases and the fatality rate. CONCLUSIONS: Reducing testing delays will help to contain outbreaks. These results provide policymakers with quantitative evidence of efficiency as a critical value in developing testing and contact tracing strategies.

A simple and easily implemented risk model to predict 1-year ischemic stroke and systemic embolism in Chinese patients with atrial fibrillation.

BACKGROUND: Accurate prediction of ischemic stroke is required for deciding anticoagulation use in patients with atrial fibrillation (AF). Even though only 6% to 8% of AF patients die from stroke, about 90% are indicated for anticoagulants according to the current AF management guidelines. Therefore, we aimed to develop an accurate and easy-to-use new risk model for 1-year thromboembolic events (TEs) in Chinese AF patients. METHODS: From the prospective China Atrial Fibrillation Registry cohort study, we identified 6601 AF patients who were not treated with anticoagulation or ablation at baseline. We selected the most important variables by the extreme gradient boosting (XGBoost) algorithm and developed a simplified risk model for predicting 1-year TEs. The novel risk score was internally validated using bootstrapping with 1000 replicates and compared with the CHA2DS2-VA score (excluding female sex from the CHA2DS2-VASc score). RESULTS: Up to the follow-up of 1 year, 163 TEs (ischemic stroke or systemic embolism) occurred. Using the XGBoost algorithm, we selected the three most important variables (congestive heart failure or left ventricular dysfunction, age, and prior stroke, abbreviated as CAS model) to predict 1-year TE risk. We trained a multivariate Cox regression model and assigned point scores proportional to model coefficients. The CAS scheme classified 30.8% (2033/6601) of the patients as low risk for TE (CAS score = 0), with a corresponding 1-year TE risk of 0.81% (95% confidence interval [CI]: 0.41%-1.19%). In our cohort, the C-statistic of CAS model was 0.69 (95% CI: 0.65-0.73), higher than that of CHA2DS2-VA score (0.66, 95% CI: 0.62-0.70, Z = 2.01, P = 0.045). The overall net reclassification improvement from CHA2DS2-VA categories (low = 0/high ≥1) to CAS categories (low = 0/high ≥1) was 12.2% (95% CI: 8.7%-15.7%). CONCLUSION: In Chinese AF patients, a novel and simple CAS risk model better predicted 1-year TEs than the widely-used CHA2DS2-VA risk score and identified a large proportion of patients with low risk of TEs, which could potentially improve anticoagulation decision-making. TRIAL REGISTRATION: www.chictr.org.cn (Unique identifier No. ChiCTR-OCH-13003729).

A Clinically Practical and Interpretable Deep Model for ICU Mortality Prediction with External Validation.

Deep learning models are increasingly studied in the field of critical care. However, due to the lack of external validation and interpretability, it is difficult to generalize deep learning models in critical care senarios. Few works have validated the performance of the deep learning models with external datasets. To address this, we propose a clinically practical and interpretable deep model for intensive care unit (ICU) mortality prediction with external validation. We use the newly published dataset Philips eICU to train a recurrent neural network model with two-level attention mechanism, and use the MIMIC III dataset as the external validation set to verify the model performance. This model achieves a high accuracy (AUC = 0.855 on the external validation set) and have good interpretability. Based on this model, we develop a system to support clinical decision-making in ICUs.

A case-control study of the determinants for cervicocerebral artery dissection.

BACKGROUND: Cervicocerebral artery dissection (CAD) is a major cause of ischemic stroke in young adults. There are many existing studies on determinants for CAD; however, they are still not totally defined. We conduct the study to further investigate the determinants for CAD based on ischemic stroke patients. METHODS: 81 ischemic stroke patients with CAD were enrolled in the CAD stroke group and 84 ischemic stroke patients without CAD were enrolled in the non-CAD stroke group. Their clinical data, such as age, gender, vascular risk factors, headache and neck pain and clinical laboratory data, were collected to analyze the differences between the two groups. RESULTS: A total of 165 ischemic stroke patients were included. The mean age of CAD stroke group was (51.6 ± 12.4) years, and (55.5 ± 8.1) years in non-CAD stroke group, with a statistically significant difference (P = 0.017). The average level of triglycerides in CAD stroke group was (1.3 ± 0.7) mmol/L, and (1.7 ± 1.1) mmol/L in non-CAD stroke group, with a statistically significant difference (P = 0.012). There were 42.0% (34/81) of headache and neck pain in CAD stroke group and 22.6% (19/84) in non-CAD stroke group, with a statistically significant difference (P = 0.008). The key findings with significant difference were stratified and multivariate logistic regression analysis showed that age < 50 years old (OR 2.98, 95% CI 1.43-6.21, P = 0.004), triglycerides < 1.6 mmol/L (OR 3.51, 95% CI 1.69-7.27, P = 0.001) and headache and neck pain (OR 2.94, 95% CI 1.39-6.20, P = 0.005) showed a positive correlation with CAD. CONCLUSION: In the process of diagnosis and treatment of ischemic stroke, for patients with age < 50 years old, headache and neck pain and triglycerides < 1.6 mmol/L, the cervicocerebral artery dissection should be considered, and vascular imaging examination needs to be performed in time.

Characteristics and relative factors of headache caused by cervicocerebral artery dissection.

OBJECTIVE: To analyze the characteristics and relative factors of headache and neck pain due to cervicocerebral artery dissection (CAD). METHODS: A total of 146 consecutive patients with CAD in Zhengzhou, China (2010-2017) were observed and registered prospectively. There were 60 (60/146) cases who complained of headache and neck pain, and we analyzed the characteristics of pain according to their clinical features. For the 130 (130/146) patients with complete clinical laboratory data, they were divided into two groups according to pain, and the relative factors of pain were analyzed. RESULTS: The headache and neck pain in 60 CAD patients was mostly acute onset (98.3%), 70.6% (12/17) of patients with anterior circulation dissection and 88.4% (38/43) of patients with posterior circulation dissection complained of moderate to severe pain. 41.2% (7/17) of patients with anterior circulation dissection had temporal pain, while 46.5% (20/43) of the patients with posterior circulation dissection had occipital pain. There were 23.5% (4/17) and 32.6% (14/43) of patients with anterior and posterior circulation dissection complained of throbbing pain, respectively, 23.5% (4/17) and 20.9% (9/43) of patients with anterior and posterior circulation dissection complained of pulsating pain. The pain could occur in the ipsilateral (40.0%), bilateral (52.7%), or contralateral (7.3%) sites of the dissection. In the 130 patients, there were 56 cases (43.1%) in the pain group, and 74 cases (56.9%) in the non-pain group. Multivariate logistic regression analysis showed that female gender (OR 4.01, 95% CI 1.63-9.85, P = 0.002), posterior circulation (OR 3.18, 95% CI 1.39-7.28, P = 0.006), history of headache (OR 4.72, 95% CI 1.08-20.52, P = 0.039), and low-density lipoprotein less than 1.8 mmol/L (OR 2.90, 95% CI 1.15-7.34, P = 0.025) were risk factors of the occurrence of the pain related to CAD. CONCLUSION: The headache and neck pain caused by CAD is a moderate to severe pain occurring suddenly. The pain nature may be diverse but mostly like throbbing and pulsating. When the dissected artery is located in the posterior circulation, the pain is mostly in the occipital region, and mostly in the temporal region when the dissected artery is located in the anterior circulation. The pain can occur in ipsilateral, bilateral, or contralateral of the dissection. In addition, several factors might contribute to the occurrence of headache and neck pain.

Integrating Multiple Data Sources and Learning Models to Predict Infectious Diseases in China.

The outbreaks of infectious diseases do not only endanger people's lives and property, but can also result in negative social impact and economic loss. Therefore, establishing early warning technologies for infectious diseases is of great value. This paper was built on the historical morbidity and mortality incidence data of infectious diseases, including typhoid fever, Hemorrhagic Fever with Renal Syndrome (HFRS), mumps, scarlatina, malaria, dysentery, pertussis, conjunctivitis, pulmonary tuberculosis, diarrhea from 2012 to 2016 in China. We also integrated search engine query data and seasonal information into the prediction models. Multiple models for prediction, including linear model, time series analysis model, boosting tree model and deep learning model (recurrent neural network, RNN) were constructed in order to predict the morbidity incidence of 10 infectious diseases. The RNN model has better predictive capability for these diseases. The improvement of techniques for infectious disease prediction can facilitate constructive and positive change towards disease prevention.

Novel, de novo dysferlin gene mutations in a patient with Miyoshi myopathy.

Miyoshi myopathy (MM) is an autosomal recessive distal muscular dystrophy caused by mutations in the dysferlin gene (DYSF), a 150-kb gene on chromosome 2p13 that contains 55 coding exons. Many patients with MM harbour mutations in the DYSF gene, and most of these mutations are inherited from the patients' parents. Recently, we encountered novel, de novo mutations in the DYSF gene in a patient with MM. DYSF gene analysis was performed by targeted next-generation sequencing, and we found that the patient had compound heterozygous mutations, including a de novo mutation (c.613C > T) in exon 6 and a novel missense mutation (c.968T > C) in exon 11. The novel missense mutation, predicted to be a disease-causing mutation or affecting protein function by MutationTaster and Polyphen2, confirmed the diagnosis. These findings provide important insights into the pathogenesis and inheritance of MM.

An Automatic Approach for Analyzing Treatment Effectiveness Based on Medication Hierarchy - The Myocardial Infarction Case Study.

Treatment effectiveness plays a fundamental role in patient therapies. In most observational studies, researchers often design an analysis pipeline for a specific treatment based on the study cohort. To evaluate other treatments in the data set, much repeated and multifarious work including cohort construction, statistical analysis need to be done. In addition, as treatments are often with an intrinsic hierarchical relationship, many rational comparable treatment pairs can be derived as new treatment variables besides the original single treatment one from the original cohort data set. In this paper, we propose an automatic treatment effectiveness analysis approach to solve this problem. With our approach, clinicians can assess the effect of treatments not only more conveniently but also more thoroughly and comprehensively. We applied this method to a real world case of estimating the drug effectiveness on Chinese Acute Myocardial Infarction (CAMI) data set and some meaningful results are obtained for potential improvement of patient treatments.