Determining Individualized Foot Progression Angle for Reduction of Knee Medial Compartment Loading During Stepping.

PURPOSE: Modifying foot progression angle (FPA), the angle between the line from the heel to the second metatarsal head and the line of progression, can reduce peak knee adduction moment (pKAM). However, determining the optimal FPA that minimizes pKAM without inducing unnatural walking patterns can be challenging. This study investigated the FPA-pKAM relationship using a robotic stepping trainer to assess the feasibility of determining the optimal FPA based on this relationship. Additionally, it examined knee moments during stepping with three different FPAs, as stepping is a recommended exercise for knee osteoarthritis (KOA) rehabilitation. METHODS: Twenty-six asymptomatic individuals stepped on a robotic stepping trainer, which measured 6-axis footplate-reaction forces/torques and three-dimensional (3-D) ankle kinematics to determine external knee moments. The robot rotated the footplates slowly (~0.5 deg/sec) between 10°-toe-out and 10°-toe-in while participants stepped continuously, unaware of the footplate rotations. The slope of pKAM-FPA relationship during continuous stepping was determined. Peak 3-D knee moments were compared between the 10°-toe-in, 0°-FPA, and 10°-toe-out FPAs with repeated-measure ANOVA. Multiple linear regression determined the covariates that predicted pKAM during stepping. RESULTS: Eighteen participants had lower pKAM and KAM impulse with 10°-toe-in than 10°-toe-out (p < 0.001) and 0°-FPA (p < 0.001 and p = 0.008, respectively) (called toe-in responders). Conversely, eight participants reduced pKAM and KAM impulse with 10°-toe-out compared to 0°-FPA (p < 0.001, p = 0.017) and 10°-toe-in (p = 0.026, p = 0.004) (called toe-out responders). A linear pKAM-FPA relationship was determined for each individual, and its slope (the pKAM rate with FPA) was positive for toe-in responders (p < 0.01) and negative for toe-out responders (p = 0.02). Regression analysis revealed that smaller pKAM with toe-in in toe-in responders was explained by increased tibia medial tilt, tibia internal rotation, footplate-reaction lateral force, footplate-reaction anterior force, and decreased footplate-reaction internal rotation torque. CONCLUSIONS: Individuals may exhibit different responses to FPA modification during stepping. The slope and intercept of the linear pKAM-FPA relationship can be determined for individual subjects. This allows for a targeted pKAM reduction through guided FPA positioning and potentially offers subject-specific precision KOA rehabilitation.

Are extra locking bolts or fibular plating more important in extreme nailing of distal tibia fractures? A cadaveric biomechanical analysis.

OBJECTIVES: In far-distal extra-articular tibia fracture "extreme" nailing, debate surrounds the relative biomechanical performance of plating the fibula compared with extra distal interlocks. This study aimed to evaluate several constructs for extreme nailing including one interlock (one medial-lateral interlock), one interlock + plate (one medial-lateral interlock with lateral fibula compression plating), and two interlocks (one medial-lateral interlock and one anterior-posterior interlock). METHODS: Fifteen pairs of fresh cadaver legs were instrumented with a tibial nail to the physeal scar. A 1 cm segment of bone was resected from the distal tibia 3.5 cm from the joint and an oblique osteotomy was made in the distal fibula. We loaded specimens with three different distal fixation constructs (one interlock, one interlock + plate, and two interlocks) through 10,000 cycles form 100N-700 N of axial loading. Load to failure (Newtons), angulation and displacement were also measured. RESULTS: Mean load to failure was 2092 N (one interlock), 1917 N (one interlock + plate), and 2545 N (two interlocks). Linear mixed effects modeling demonstrated that two interlocks had a load to failure 578 N higher than one interlock alone (95 % CI, 74N-1082 N; P = 0.02), but demonstrated no significant difference between one interlock and one interlock + plate. No statistically significant difference in rates or timing of displacement >2 mm or angulation >10° were demonstrated. CONCLUSIONS: When nailing far-distal extra-articular tibia and fibula fractures, adding a second interlock provides more stability than adding a fibular plate. Distal fibula plating may have minimal biomechanical effect in extreme nailing.

SERT-StructNet: Protein secondary structure prediction method based on multi-factor hybrid deep model.

Protein secondary structure prediction (PSSP) is a pivotal research endeavour that plays a crucial role in the comprehensive elucidation of protein functions and properties. Current prediction methodologies are focused on deep-learning techniques, particularly focusing on multi-factor features. Diverging from existing approaches, in this study, we placed special emphasis on the effects of amino acid properties and protein secondary structure propensity scores (SSPs) on secondary structure during the meticulous selection of multi-factor features. This differential feature-selection strategy results in a distinctive and effective amalgamation of the sequence and property features. To harness these multi-factor features optimally, we introduced a hybrid deep feature extraction model. The model initially employs mechanisms such as dilated convolution (D-Conv) and a channel attention network (SENet) for local feature extraction and targeted channel enhancement. Subsequently, a combination of recurrent neural network variants (BiGRU and BiLSTM), along with a transformer module, was employed to achieve global bidirectional information consideration and feature enhancement. This approach to multi-factor feature input and multi-level feature processing enabled a comprehensive exploration of intricate associations among amino acid residues in protein sequences, yielding a Q3 accuracy of 84.9% and an Sov score of 85.1%. The overall performance surpasses that of the comparable methods. This study introduces a novel and efficient method for determining the PSSP domain, which is poised to deepen our understanding of the practical applications of protein molecular structures.

Virome diversity shaped by genetic evolution and ecological landscape of Haemaphysalis longicornis.

BACKGROUND: Haemaphysalis longicornis is drawing attentions for its geographic invasion, extending population, and emerging disease threat. However, there are still substantial gaps in our knowledge of viral composition in relation to genetic diversity of H. longicornis and ecological factors, which are important for us to understand interactions between virus and vector, as well as between vector and ecological elements. RESULTS: We conducted the meta-transcriptomic sequencing of 136 pools of H. longicornis and identified 508 RNA viruses of 48 viral species, 22 of which have never been reported. Phylogenetic analysis of mitochondrion sequences divided the ticks into two genetic clades, each of which was geographically clustered and significantly associated with ecological factors, including altitude, precipitation, and normalized difference vegetation index. The two clades showed significant difference in virome diversity and shared about one fifth number of viral species that might have evolved to "generalists." Notably, Bandavirus dabieense, the pathogen of severe fever with thrombocytopenia syndrome was only detected in ticks of clade 1, and half number of clade 2-specific viruses were aquatic-animal-associated. CONCLUSIONS: These findings highlight that the virome diversity is shaped by internal genetic evolution and external ecological landscape of H. longicornis and provide the new foundation for promoting the studies on virus-vector-ecology interaction and eventually for evaluating the risk of H. longicornis for transmitting the viruses to humans and animals. Video Abstract.

Changes in Cytokine Levels in Patients with Severe Fever with Thrombocytopenia Syndrome Virus.

Purpose: To characterize the cytokine profile of patients with severe fever with thrombocytopenia syndrome (SFTS) in relation to disease severity. Patients and Methods: 60 laboratory-confirmed SFTS patients and 12 healthy individuals from multi-centers in Shandong Province of China were included, and all patients were divided into fatal patients (9) and recovered patients (51) due to their final outcomes. Multiplex-microbead immunoassays were conducted to estimate levels of 27 cytokines in the sera of patients and controls. Results: The results showed that levels of IL-2, IL-4, IL-6, IL-7, IL-8, IL-15, IL-1RA, G-CSF, GM-CSF, IFN-γ, TNF-α, basic FGF, PDGF-BB, RANTES, IP-10, MIP-1α, MIP-1ß, MCP-1, and Eotaxin differed significantly among the SFTS fatal patients, recovered patients, and the healthy controls (all p<0.05). Compared to the healthy controls, the fatal patients and recovered patients had reduced levels of IL-2, IL-4, IL-7, PDGF-BB, RANTES, and Eotaxin, while the levels of PDGF-BB and RANTES were significantly lower in fatal patients compared to recovered patients. The increasing levels of IL-6, IL-8, IL-15, IL-1RA, G-CSF, GM-CSF, IFN-γ, TNF-α, basic FGF, IP-10, MIP-1α, MIP-1ß, and MCP-1 were observed in fatal patients (all p<0.05), and the levels of IL-6, IP-10, MIP-1α, and MCP-1 were significantly higher than other two groups. The Spearman correlation analysis indicated a positive correlation between platelet count and PDGF-BB levels (p<0.05), while the white blood cell count had a negative correlation with MIP-1 level (p<0.05). Conclusion: The research exhibited that the SFTS virus (SFTSV) caused an atypical manifestation of cytokines. The levels of IL-6, IP-10, MIP-1α, and MCP-1 had been observed a positive association with the severity of the illness.

Biomechanical Comparison of Four- versus Six-Strand Transosseous Suture Repair for Patellar Tendon Rupture.

BACKGROUND: Surgical repair is indicated for patellar tendon ruptures that result in loss of knee extensor mechanism function. However, biomechanical studies report conflicting results when comparing transosseous suture versus suture anchor repair techniques. This discrepancy may be due to inconsistencies in experimental design as these studies use various numbers of suture strands. Therefore, the main objective of this study is to compare the ultimate load of four- versus six-strand transosseous suture repair. Secondary objectives are to compare gap formation after cyclical loading and mode of failure. METHODS: Six pairs of fresh-frozen cadaveric specimen were randomly allocated to either four- or six-strand transosseous suture repair. Specimen underwent preconditioning cyclical loading and then load to failure. RESULTS: The six-strand repair had a significantly higher maximum load to failure compared with the four-strand repair (mean difference = 319.3 N [57.9%], p = 0.03). There was no significant difference in gap length after cyclical loading or at max load. There were no significant differences in mode of failure. CONCLUSION: Utilizing a six-stand transosseous patella tendon repair construct with one additional suture increases overall construct strength by over 50% compared with a four-strand construct.

Multi-joint Assessment of Proprioception Impairments Poststroke.

OBJECTIVES: To investigate shoulder, elbow and wrist proprioception impairment poststroke. DESIGN: Proprioceptive acuity in terms of the threshold detection to passive motion at the shoulder, elbow and wrist joints was evaluated using an exoskeleton robot to the individual joints slowly in either inward or outward direction. SETTING: A university research laboratory. PARTICIPANTS: Seventeen stroke survivors and 17 healthy controls (N=34). Inclusion criteria of stroke survivors were (1) a single stroke; (2) stroke duration <1 year; and (3) cognitive ability to follow simple instructions. INTERVENTIONS: Not applicable. MAIN OUTCOME MEASURES: Threshold detection to passive motion and detection error at the shoulder, elbow and wrist. RESULTS: There was significant impairment of proprioceptive acuity in stroke survivors as compared to healthy group at all 3 joints and in both the inward (shoulder horizontal adduction, elbow and wrist flexion, P<.01) and outward (P<.01) motion. Furthermore, the distal wrist joint showed more severe impairment in proprioception than the proximal shoulder and elbow joints poststroke (P<.01) in inward motion. Stroke survivors showed significantly larger detection error in identifying the individual joint in motion (P<.01) and the movement direction (P<.01) as compared to the healthy group. There were significant correlations among the proprioception acuity across the shoulder, elbow and wrist joints and 2 movement directions poststroke. CONCLUSIONS: There were significant proprioceptive sensory impairments across the shoulder, elbow and wrist joints poststroke, especially at the distal wrist joint. Accurate evaluations of multi-joint proprioception deficit may help guide more focused rehabilitation.

MDformer: A transformer-based method for predicting miRNA-Disease associations using multi-source feature fusion and maximal meta-path instances encoding.

There is a growing body of evidence suggesting that microRNAs (miRNAs), small biological molecules, play a crucial role in the diagnosis, treatment, and prognostic assessment of diseases. However, it is often inefficient to verify the association between miRNAs and diseases (MDA) through traditional experimental methods. Based on this situation, researchers have proposed various computational-based methods, but the existing methods often have many drawbacks in terms of predictive effectiveness and accuracy. Therefore, in order to improve the prediction performance of computational methods, we propose a transformer-based prediction model (MDformer) for multi-source feature information. Specifically, first, we consider multiple features of miRNAs and diseases from the molecular biology perspective and utilize them in a fusion. Then high-quality node feature embeddings were generated using a feature encoder based on the transformer architecture and meta-path instances. Finally, a deep neural network was built for MDA prediction. To evaluate the performance of our model, we performed multiple 5-fold cross-validations as well as comparison experiments on HMDD v3.2 and HMDD v2.0 databases, and the experimental results of the average ROC area under the curve (AUC) were higher than the comparative methods for both databases at 0.9506 and 0.9369. We conducted case studies on five highly lethal cancers (breast, lung, colorectal, gastric, and hepatocellular cancers), and the first 30 predictions for these five diseases achieved 97.3% accuracy. In conclusion, MDformer is a reliable and scientifically sound tool that can be used to accurately predict MDA. In addition, the source code is available at https://github.com/Linda908/MDformer.

DAEMDA: A Method with Dual-Channel Attention Encoding for miRNA-Disease Association Prediction.

A growing number of studies have shown that aberrant microRNA (miRNA) expression is closely associated with the evolution and development of various complex human diseases. These key biomarkers' identification and observation are significant for gaining a deeper understanding of disease pathogenesis and therapeutic mechanisms. Consequently, pinpointing potential miRNA-disease associations (MDA) has become a prominent bioinformatics subject, encouraging several new computational methods given the advances in graph neural networks (GNN). Nevertheless, these existing methods commonly fail to exploit the network nodes' global feature information, leaving the generation of high-quality embedding representations using graph properties as a critical unsolved issue. Addressing these challenges, we introduce the DAEMDA, a computational method designed to optimize the current models' efficacy. First, we construct similarity and heterogeneous networks involving miRNAs and diseases, relying on experimentally corroborated miRNA-disease association data and analogous information. Then, a newly-fashioned parallel dual-channel feature encoder, designed to better comprehend the global information within the heterogeneous network and generate varying embedding representations, follows this. Ultimately, employing a neural network classifier, we merge the dual-channel embedding representations and undertake association predictions between miRNA and disease nodes. The experimental results of five-fold cross-validation and case studies of major diseases based on the HMDD v3.2 database show that this method can generate high-quality embedded representations and effectively improve the accuracy of MDA prediction.

DeepICSH: a complex deep learning framework for identifying cell-specific silencers and their strength from the human genome.

Silencers are noncoding DNA sequence fragments located on the genome that suppress gene expression. The variation of silencers in specific cells is closely related to gene expression and cancer development. Computational approaches that exclusively rely on DNA sequence information for silencer identification fail to account for the cell specificity of silencers, resulting in diminished accuracy. Despite the discovery of several transcription factors and epigenetic modifications associated with silencers on the genome, there is still no definitive biological signal or combination thereof to fully characterize silencers, posing challenges in selecting suitable biological signals for their identification. Therefore, we propose a sophisticated deep learning framework called DeepICSH, which is based on multiple biological data sources. Specifically, DeepICSH leverages a deep convolutional neural network to automatically capture biologically relevant signal combinations strongly associated with silencers, originating from a diverse array of biological signals. Furthermore, the utilization of attention mechanisms facilitates the scoring and visualization of these signal combinations, whereas the employment of skip connections facilitates the fusion of multilevel sequence features and signal combinations, thereby empowering the accurate identification of silencers within specific cells. Extensive experiments on HepG2 and K562 cell line data sets demonstrate that DeepICSH outperforms state-of-the-art methods in silencer identification. Notably, we introduce for the first time a deep learning framework based on multi-omics data for classifying strong and weak silencers, achieving favorable performance. In conclusion, DeepICSH shows great promise for advancing the study and analysis of silencers in complex diseases. The source code is available at https://github.com/lyli1013/DeepICSH.

MCANet: shared-weight-based MultiheadCrossAttention network for drug-target interaction prediction.

Accurate and effective drug-target interaction (DTI) prediction can greatly shorten the drug development lifecycle and reduce the cost of drug development. In the deep-learning-based paradigm for predicting DTI, robust drug and protein feature representations and their interaction features play a key role in improving the accuracy of DTI prediction. Additionally, the class imbalance problem and the overfitting problem in the drug-target dataset can also affect the prediction accuracy, and reducing the consumption of computational resources and speeding up the training process are also critical considerations. In this paper, we propose shared-weight-based MultiheadCrossAttention, a precise and concise attention mechanism that can establish the association between target and drug, making our models more accurate and faster. Then, we use the cross-attention mechanism to construct two models: MCANet and MCANet-B. In MCANet, the cross-attention mechanism is used to extract the interaction features between drugs and proteins for improving the feature representation ability of drugs and proteins, and the PolyLoss loss function is applied to alleviate the overfitting problem and the class imbalance problem in the drug-target dataset. In MCANet-B, the robustness of the model is improved by combining multiple MCANet models and prediction accuracy further increases. We train and evaluate our proposed methods on six public drug-target datasets and achieve state-of-the-art results. In comparison with other baselines, MCANet saves considerable computational resources while maintaining accuracy in the leading position; however, MCANet-B greatly improves prediction accuracy by combining multiple models while maintaining a balance between computational resource consumption and prediction accuracy.

A deep learning based two-layer predictor to identify enhancers and their strength.

The enhancer is a DNA sequence that can increase the activity of promoters and thus speed up the frequency of gene transcription. The enhancer plays an essential role in activating gene expression. Currently, gene sequencing technology has been developed for 30 years from the first generation to the third generation, and a variety of biological sequence data have increased significantly every year. Due to the importance of enhancer functions, it is very expensive to identify enhancers through biochemical experiments. Therefore, we need to study new methods for the identification and classification of enhancers. Based on the K-mer principle this study proposed a feature extraction method that others have not used in convolutional neural networks. Then, we combined it with one-hot encoding to build an efficient one-dimensional convolutional neural network ensemble model for predicting enhancers and their strengths. Finally, we used five commonly used classification problem evaluation indicators to compare with the models proposed by other researchers. The model proposed in this paper has a better performance by using the same independent test dataset as other models.

Open reduction internal fixation of rib fractures: a biomechanical comparison between the RibLoc U Plus® system and anterior plate in rib implants.

OBJECTIVES: In this study, we assessed the bending strength of two surgical repairs of rib fracture using RibLoc® U Plus system made by Acute Innovations and the anterior plate by Synthes. METHODS: After a rib fracture was created in seven pairs of cadaveric rib specimens, one side was repaired with the anterior plate and the other side repaired with the RibLoc U Plus® plate. Each of the rib is loaded using a custom device over 360,000 bending cycles to simulate in vivo fatiguing related to respiration. Upon completion of the cyclic loading, the specimens were compressively loaded to failure and the failure bending moment was determined. RESULTS: The ribs repaired with the RibLoc U Plus® system showed 79% higher failure bending moment than that of the anterior plate, with a p value of 0.033. The ribs repaired with RibLoc U Plus® showed a trend of less stiffness reduction over the 360,000 loading cycles. CONCLUSION: The biomechanical study showed that the RibLoc U Plus® system is stronger in the bending moment loading of repaired ribs, possibly due to the U-shape structure supporting both the inner and outer cortices of a repaired rib.

Identification of adaptor proteins by incorporating deep learning and PSSM profiles.

Adaptor proteins, also known as signal transduction adaptor proteins, are important proteins in signal transduction pathways, and play a role in connecting signal proteins for signal transduction between cells. Studies have shown that adaptor proteins are closely related to some diseases, such as tumors and diabetes. Therefore, it is very meaningful to construct a relevant model to accurately identify adaptor proteins. In recent years, many studies have used a position-specific scoring matrix (PSSM) and neural network methods to identify adaptor proteins. However, ordinary neural network models cannot correlate the contextual information in PSSM profiles well, so these studies usually process 20×N (N > 20) PSSM into 20×20 dimensions, which results in the loss of a large amount of protein information; This research proposes an efficient method that combines one-dimensional convolution (1-D CNN) and a bidirectional long short-term memory network (biLSTM) to identify adaptor proteins. The complete PSSM profiles are the input of the model, and the complete information of the protein is retained during the training process. We perform cross-validation during model training and test the performance of the model on an independent test set; in the data set with 1224 adaptor proteins and 11,078 non-adaptor proteins, five indicators including specificity, sensitivity, accuracy, area under the receiver operating characteristic curve (AUC) metric and Matthews correlation coefficient (MCC), were employed to evaluate model performance. On the independent test set, the specificity, sensitivity, accuracy and MCC were 0.817, 0.865, 0.823 and 0.465, respectively. Those results show that our method is better than the state-of-the art methods. This study is committed to improve the accuracy of adaptor protein identification, and laid a foundation for further research on diseases related to adaptor protein. This research provided a new idea for the application of deep learning related models in bioinformatics and computational biology.

Association of mental distress with chronic diseases in 1.9 million individuals: A population-based cross-sectional study.

OBJECTIVE: Mental distress has a high global prevalence and is associated with poor health outcomes. This study aimed to estimate the relationship between mental distress and the risk of 10 chronic diseases using data from the Behavioral Risk Factor Surveillance System (BRFSS). METHODS: Cross-sectional data from the 2013, 2014, 2015, 2016 and 2017 BRFSS were analyzed. The association between mental distress based on the number of days of poor mental health and the risk of 10 chronic diseases, namely obesity, diabetes, asthma, chronic obstructive pulmonary disease (COPD), arthritis, kidney disease, coronary heart disease (CHD), stroke, skin cancer, and other cancers, were assessed by logistic regression models to calculate odds ratios and 95% confidence intervals. Subgroup analyses stratified by age and sex were also conducted. RESULTS: Positive associations between mental distress and chronic diseases were observed. We also found a dose-response gradient between mental distress levels and the risk of all chronic diseases except skin cancer. In respondents aged 18-44 years reporting ≥23 days/month of mental distress, there has the largest odds ratio between mental distress levels and each chronic disease. Moreover, mental distress was associated with higher risks of obesity and arthritis in women relative to men. CONCLUSIONS: Mental distress was positively associated with chronic diseases. Age and sex are crucial in this relationship. Further studies with longitudinal data are needed to clarify the direction of this association.

CRISPRCasStack: a stacking strategy-based ensemble learning framework for accurate identification of Cas proteins.

CRISPR-Cas system is an adaptive immune system widely found in most bacteria and archaea to defend against exogenous gene invasion. One of the most critical steps in the study of exploring and classifying novel CRISPR-Cas systems and their functional diversity is the identification of Cas proteins in CRISPR-Cas systems. The discovery of novel Cas proteins has also laid the foundation for technologies such as CRISPR-Cas-based gene editing and gene therapy. Currently, accurate and efficient screening of Cas proteins from metagenomic sequences and proteomic sequences remains a challenge. For Cas proteins with low sequence conservation, existing tools for Cas protein identification based on homology cannot guarantee identification accuracy and efficiency. In this paper, we have developed a novel stacking-based ensemble learning framework for Cas protein identification, called CRISPRCasStack. In particular, we applied the SHAP (SHapley Additive exPlanations) method to analyze the features used in CRISPRCasStack. Sufficient experimental validation and independent testing have demonstrated that CRISPRCasStack can address the accuracy deficiencies and inefficiencies of the existing state-of-the-art tools. We also provide a toolkit to accurately identify and analyze potential Cas proteins, Cas operons, CRISPR arrays and CRISPR-Cas locus in prokaryotic sequences. The CRISPRCasStack toolkit is available at https://github.com/yrjia1015/CRISPRCasStack.

Heterogeneity Analysis of Bladder Cancer Based on DNA Methylation Molecular Profiling.

Bladder cancer is a highly complex and heterogeneous malignancy. Tumor heterogeneity is a barrier to effective diagnosis and treatment of bladder cancer. Human carcinogenesis is closely related to abnormal gene expression, and DNA methylation is an important regulatory factor of gene expression. Therefore, it is of great significance for bladder cancer research to characterize tumor heterogeneity by integrating genetic and epigenetic characteristics. This study explored specific molecular subtypes based on DNA methylation status and identified subtype-specific characteristics using patient samples from the TCGA database with DNA methylation and gene expression were measured simultaneously. The results were validated using an independent cohort from GEO database. Four DNA methylation molecular subtypes of bladder cancer were obtained with different prognostic states. In addition, subtype-specific DNA methylation markers were identified using an information entropy-based algorithm to represent the unique molecular characteristics of the subtype and verified in the test set. The results of this study can provide an important reference for clinicians to make treatment decisions.

Psychological Distress and All-Cause, Cardiovascular Disease, Cancer Mortality Among Adults with and without Diabetes.

AIM: To examine the association of psychological distress with all-cause, cardiovascular disease (CVD) and cancer mortality in US adults, and verified whether the associations differed between participants with and without diabetes. METHODS: A total of 485,864 adults (446,288 without diabetes and 39,576 with diabetes) who participated in the National Health Interview Survey from 1997 to 2013 were linked to the National Death Index through December 31, 2015. Psychological distress was measured by the Kessler 6 distress scale (K6). Multivariable Cox proportional hazards regression models were performed to estimate hazard ratios (HR) and 95% confidence intervals (95% CI) for the association between psychological distress and mortality. RESULTS: We ascertained 11,746 deaths (mean follow-up, 7. 7 years) among people with diabetes and 51,636 deaths (9.9 years) among those without diabetes. Psychological distress was associated with higher all-cause, CVD, and cancer mortality. Compared to non-diabetic adults without psychological distress, HRs (95% CI) were 1.07 (1.04 to 1.09) for mild, 1.26 (1.22 to 1.30) for moderate and 1.46 (1.38 to 1.55) for severe psychological distress. Compared to the same reference group, in diabetic participants the HRs were 1.39 (1.33 to 1.44) for no psychological distress, 1.59 (1.53 to 1.66) for mild, 1.90 (1.80 to 2.00) for moderate and 1.98 (1.82 to 2.17) for severe psychological distress. Similar associations were also observed for CVD and cancer mortality but with non-statistically significant interaction. CONCLUSION: Psychological distress was associated with higher mortality, particularly in participants with diabetes. Strategies to ameliorate psychological distress may be important to reduce mortality in this population.

Detection of Novel Spotted Fever Group Rickettsiae (Rickettsiales: Rickettsiaceae) in Ticks (Acari: Ixodidae) in Southwestern China.

Spotted fever group rickettsiae, mainly maintained and transmitted by ticks, are important etiological agents of (re)emerging zoonotic diseases worldwide. It is of great significance to investigate spotted fever group rickettsiae in ticks in different areas for the prevention and control of rickettsioses. In this study, a total of 305 ticks were collected from wild and domestic animals in Chongqing, Guizhou, Yunnan, and Guangxi provinces of southwestern China during 2017-2019 and examined for the presence of spotted fever group rickettsiae by PCR with primers targeting the partial gltA, ompA, rrs, and htrA genes. Results showed that two spotted fever group rickettsiae species, including the pathogenic Candidatus Rickettsia jingxinensis (Rickettsiales: Rickettsiaceae) and a potential novel species Rickettsia sp. sw (Rickettsiales: Rickettsiaceae), were identified. The Ca. R. jingxinensis sequences were recovered from Rhipicephalus microplus (Ixodida: Ixodidae) and Haemaphysalis longicornis (Ixodida: Ixodidae) ticks and phylogenetically clustered with previous Ca. R. jingxinensis, Ca. R. longicornii (Rickettsiales: Rickettsiaceae), and Rickettsia sp. XY118 (Rickettsiales: Rickettsiaceae) strains. Rickettsia sp. sw was detected in Amblyomma geoemydae (Ixodida: Ixodidae) and Rh. microplus. Interestingly, as far as we know, this was the first report of Rickettsia (Rickettsiales: Rickettsiaceae) in A. geoemydae. Phylogenetic analyses indicated that this potential novel species was closely related to R. aeschlimannii (Rickettsiales: Rickettsiaceae) with gltA and ompA genes and grouped in a cluster composed of R. montanensis (Rickettsiales: Rickettsiaceae), R. raoultii (Rickettsiales: Rickettsiaceae), R. aeschlimannii, R. massiliae (Rickettsiales: Rickettsiaceae), and R. rhipicephali (Rickettsiales: Rickettsiaceae) with htrA, while formed a separate clade with rrs. The pathogenicity of Rickettsia sp. sw should be further confirmed. These results expand the knowledge of the geographical distribution and vector distribution of spotted fever group rickettsiae in China and are useful for assessing the potential public health risk.

Prevalence of Multiple Tick-Borne Pathogens in Various Tick Vectors in Northeastern China.

Background: Tick-borne bacteria and protozoa can cause a variety of human and animal diseases in China. It is of great importance to monitor the prevalence and dynamic variation of these pathogens in ticks in ever-changing natural and social environment. Materials and Methods: Ticks were collected from Heilongjiang and Jilin provinces of northeastern China during 2018-2019 followed by morphological identification. The presence of Rickettsia spp., Anaplasma spp., Ehrlichia spp., Borrelia spp., Babesia spp., and Theileria spp. was examined by PCR and Sanger sequencing. The obtained sequences were subjected to phylogenetic analysis through Mega 7.0. Statistical analysis was performed using SPSS 24.0. Results: A total of 250 ticks from 5 species of 3 genera were collected. Ixodes and Haemaphysalis ticks carried more species of pathogens than Dermacentor, and the pathogens detected in Haemaphysalis japonica varied significantly among different sampling sites. The infection rates of Rickettsia spp., Anaplasma spp., Ehrlichia spp., Borrelia spp., Babesia spp., and Theileria spp. were 41.2%, 0, 2.0%, 7.2%, 1.2%, and 7.2%, respectively. Twelve pathogens were identified, among which Rickettsia raoultii (29.6%), Candidatus Rickettsia tarasevichiae (9.2%), and Theileria equi (4.4%) were the three most common ones. Rickettsia had its dominant vector, that is, R. raoultii had high infection rates in Dermacentor nuttalli and Dermacentor silvarum, Ca. R. tarasevichiae in Ixodes persulcatus, and Rickettsia heilongjiangensis in H. japonica. Interestingly, unclassified species were observed, including a Rickettsia sp., an Ehrlichia sp., a Borrelia sp., and a Babesia sp. Coinfections with different pathogens were identified in 9.2% of all tested ticks, with I. persulcatus most likely to be coinfected (23.8%) and Rickettsia spp. and Borrelia spp. as the most common combination (16.7%). Conclusions: The results of this study reflect high diversity and complexity of pathogens in ticks, which are useful for designing more targeted and effective control measures for tick-borne diseases in China.