Development of a screening system of gene sets for estimating the time of early skeletal muscle injury based on second-generation sequencing technology.

The present study is aimed to address the challenge of wound age estimation in forensic science by identifying reliable genetic markers using low-cost and high-precision second-generation sequencing technology. A total of 54 Sprague-Dawley rats were randomly assigned to a control group or injury groups, with injury groups being further divided into time points (4 h, 8 h, 12 h, 16 h, 20 h, 24 h, 28 h, and 32 h after injury, n = 6) to establish rat skeletal muscle contusion models. Gene expression data were obtained using second-generation sequencing technology, and differential gene expression analysis, weighted gene co-expression network analysis (WGCNA) and time-dependent expression trend analysis were performed. A total of six sets of biomarkers were obtained: differentially expressed genes at adjacent time points (127 genes), co-expressed genes most associated with wound age (213 genes), hub genes exhibiting time-dependent expression (264 genes), and sets of transcription factors (TF) corresponding to the above sets of genes (74, 87, and 99 genes, respectively). Then, random forest (RF), support vector machine (SVM) and multilayer perceptron (MLP), were constructed for wound age estimation from the above gene sets. The results estimated by transcription factors were all superior to the corresponding hub genes, with the transcription factor group of WGCNA performed the best, with average accuracy rates of 96% for three models' internal testing, and 91.7% for the highest external validation. This study demonstrates the advantages of the indicator screening system based on second-generation sequencing technology and transcription factor level for wound age estimation.

Machine-learning model for the prediction of acute orthostatic hypotension after levodopa administration.

BACKGROUND: Levodopa could induce orthostatic hypotension (OH) in Parkinson's disease (PD) patients. Accurate prediction of acute OH post levodopa (AOHPL) is important for rational drug use in PD patients. Here, we develop and validate a prediction model of AOHPL to facilitate physicians in identifying patients at higher probability of developing AOHPL. METHODS: The study involved 497 PD inpatients who underwent a levodopa challenge test (LCT) and the supine-to-standing test (STS) four times during LCT. Patients were divided into two groups based on whether OH occurred during levodopa effectiveness (AOHPL) or not (non-AOHPL). The dataset was randomly split into training (80%) and independent test data (20%). Several models were trained and compared for discrimination between AOHPL and non-AOHPL. Final model was evaluated on independent test data. Shapley additive explanations (SHAP) values were employed to reveal how variables explain specific predictions for given observations in the independent test data. RESULTS: We included 180 PD patients without AOHPL and 194 PD patients with AOHPL to develop and validate predictive models. Random Forest was selected as our final model as its leave-one-out cross validation performance [AUC_ROC 0.776, accuracy 73.6%, sensitivity 71.6%, specificity 75.7%] outperformed other models. The most crucial features in this predictive model were the maximal SBP drop and DBP drop of STS before medication (ΔSBP/ΔDBP). We achieved a prediction accuracy of 72% on independent test data. ΔSBP, ΔDBP, and standing mean artery pressure were the top three variables that contributed most to the predictions across all individual observations in the independent test data. CONCLUSIONS: The validated classifier could serve as a valuable tool for clinicians, offering the probability of a patient developing AOHPL at an early stage. This supports clinical decision-making, potentially enhancing the quality of life for PD patients.

Construction of a Stable Expression System Based on the Endogenous hbpB/hbpC Toxin-Antitoxin System of Halomonas bluephagenesis.

Halomonas bluephagenesis is a halophilic bacterium capable of efficiently producing polyhydroxyalkanoates and other valuable chemicals through high salinity open fermentation, offering an appealing platform for next-generation industrial biotechnology. Various techniques have been developed to engineer Halomonas bluephagenesis, each with its inherent shortcomings. Genome editing methods often entail complex and time-consuming processes, while flexible expression systems relying on plasmids necessitate the use of antibiotics. In this study, we developed a stable recombinant plasmid vector, pHbPBC, based on a novel hbpB/hbpC toxin-antitoxin system found within the endogenous plasmid of Halomonas bluephagenesis. Remarkably, pHbPBC exhibited exceptional stability during 7 days of continuous subculture, eliminating the need for antibiotics or other selection pressures. This stability even rivaled genomic integration, all while achieving higher levels of heterologous expression. Our research introduces a novel approach for genetically modifying and harnessing nonmodel halophilic bacteria, contributing to the advancement of next-generation industrial biotechnology.

Risk Factors for Nodal Failure in Patients with FIGO IIIC Cervical Cancer Receiving Definitive Image-Guided Radiotherapy.

BACKGROUND: Nodal failure is a major failure pattern for patients with FIGO IIIC cervical cancer, which is further associated with worse survival. This study was designed to investigate risk factors for nodal failure in FIGO IIIC cervical cancer patients. METHODS: The characteristics of positive lymph nodes (LNs) and relevant clinical factors of 162 FIGO IIIC cervical cancer patients were collected. The chi-square test and logistic regression model were used to identify risk factors for nodal failure. RESULTS: In total, 368 positive LNs were identified, including 307 pelvic LNs and 61 para-aortic LNs. The nodal failure rates for all LNs, pelvic LNs, and para-aortic LNs were 9.2%, 7.8%, and 16.4%, respectively. After 20 fractions of RT, a nodal short diameter (D20F) ≥ 0.95 cm and a ratio of nodal shrinkage (ΔV20F) < 0.435 resulted; <4 cycles of chemotherapy indicated higher nodal failure rates for all LNs. For pelvic LNs, ΔV20F < 0.435 and <4 cycles of chemotherapy were associated with a higher incidence of nodal failure. For para-aortic LNs, ΔV20F < 0.435 was the only risk factor for nodal failure. CONCLUSIONS: Para-aortic LNs were more likely to experience nodal failure than pelvic LNs. Nodal shrinkage during radiotherapy and cycles of chemotherapy were associated with nodal failure in patients with FIGO IIIC cervical cancer.

Optimal cisplatin cycles in locally advanced cervical carcinoma patients treated with concurrent chemoradiotherapy

Purpose To analyze the effect of cisplatin cycles on the clinical outcomes of patients with locally advanced cervical cancer (LACC) treated with concurrent chemoradiotherapy (CCRT). Methods This study included 749 patients with LACC treated with CCRT between January 2011 and December 2015. A receiver operating characteristic (ROC) curve was used to analyze the optimal cut-off of cisplatin cycles in predicting clinical outcomes. Clinicopathological features of the patients were compared using the Chi-square test. Prognosis was assessed using log-rank tests and Cox proportional hazard models. Toxicities were compared among different cisplatin cycle groups. Results Based on the ROC curve, the optimal cut-off of the cisplatin cycles was 4.5 (sensitivity, 64.3%; specificity, 54.3%). The 3-year overall, disease-free, loco-regional relapse-free, and distant metastasis-free survival for patients with low-cycles (cisplatin cycles < 5) and high-cycles (≥ 5) were 81.5% and 89.0% (P < 0.001), 73.4% and 80.1% (P = 0.024), 83.0% and 90.8% (P = 0.005), and 84.9% and 86.8% (P = 0.271), respectively. In multivariate analysis, cisplatin cycles were an independent prognostic factor for overall survival. In the subgroup analysis of high-cycle patients, patients who received over five cisplatin cycles had similar overall, disease-free, loco-regional relapse-free, and distant metastasis-free survival to patients treated with five cycles. Acute and late toxicities were not different between the two groups. Conclusion Cisplatin cycles were associated with overall, disease-free, and loco-regional relapse-free survival in LACC patients who received CCRT. Five cycles appeared to be the optimal number of cisplatin cycles during CCRT (AU)

Combined metabolomics and tandem machine-learning models for wound age estimation: a novel analytical strategy.

Wound age estimation is one of the most challenging and indispensable issues for forensic pathologists. Although many methods based on physical findings and biochemical tests can be used to estimate wound age, an objective and reliable method for inferring the time interval after injury remains difficult. In the present study, endogenous metabolites of contused skeletal muscle were investigated to estimate the time interval after injury. Animal model of skeletal muscle injury was established using Sprague-Dawley rat, and the contused muscles were sampled at 4, 8, 12, 16, 20, 24, 28, 32, 36, 40, 44, and 48 h postcontusion (n = 9). Then, the samples were analysed using ultraperformance liquid chromatography coupled with high-resolution mass spectrometry. A total of 43 differential metabolites in contused muscle were determined by metabolomics method. They were applied to construct a two-level tandem prediction model for wound age estimation based on multilayer perceptron algorithm. As a result, all muscle samples were eventually divided into the following subgroups: 4, 8, 12, 16-20, 24-32, 36-40, and 44-48 h. The tandem model exhibited a robust performance and achieved a prediction accuracy of 92.6%, which was much higher than that of the single model. In summary, the multilayer perceptron-multilayer perceptron tandem machine-learning model based on metabolomics data can be used as a novel strategy for wound age estimation in future forensic casework. Key Points: The changes of metabolite profile were correlated with the time interval after injury in contused skeletal muscle.A panel of 43 endogenous metabolites screened by ultraperformance liquid chromatography coupled with high-resolution mass spectrometry could distinguish the wound ages.The multilayer perceptron (MLP) algorithm exhibited a robust performance in wound age estimation using metabolites.The combination of matabolomics and MLP-MLP tandem model could improve the accuracy of inferring the time interval after injury.

[Research Progress of Carboxymethyl Chitosan-Based Haemostatic Materials and Their Haemostatic Mechanism --Review].

Effective haemostatic materials can quickly control bleeding and achieve the purpose of saving patients' lives. In recent years, chitosan-based haemostatic materials have shown good haemostatic effects, but their application is limited because chitosan is almost insoluble in water. Carboxymethyl chitosan-based haemostatic materials can promote hemostasis by activating red blood cells and aggregating platelets. In addition, carboxymethyl chitosan can bind with Ca2+ to activate platelets and coagulation factors, and start endogenous coagulation pathways, which can adsorb fibrinogen in plasma to promote haemostasis. In this paper, the latest research progress of carboxymethyl chitosan-based haemostatic materials and their haemostatic mechanism were reviewed, in order to further strengthen the understanding of the haemostatic mechanism of carboxymethyl chitosan-based haemostatic materials, and provide new idea for the research and clinical application of carboxymethyl chitosan-based haemostatic materials.

Exploring postmortem succession of rat intestinal microbiome for PMI based on machine learning algorithms and potential use for humans.

The microbial communities may undergo a meaningful successional change during the progress of decay and decomposition that could aid in determining the post-mortem interval (PMI). However, there are still challenges to applying microbiome-based evidence in law enforcement practice. In this study, we attempted to investigate the principles governing microbial community succession during decomposition of rat and human corpse, and explore their potential use for PMI of human cadavers. A controlled experiment was conducted to characterize temporal changes in microbial communities associated with rat corpses as they decomposed for 30 days. Obvious differences of microbial community structures were observed among different stages of decomposition, especially between decomposition of 0-7d and 9-30d. Thus, a two-layer model for PMI prediction was developed based on the succession of bacteria by combining classification and regression models using machine learning algorithms. Our results achieved 90.48% accuracy for discriminating groups of PMI 0-7d and 9-30d, and yielded a mean absolute error of 0.580d within 7d decomposition and 3.165d within 9-30d decomposition. Furthermore, samples from human cadavers were collected to gain the common succession of microbial community between rats and humans. Based on the 44 shared genera of rats and humans, a two-layer model of PMI was rebuilt to be applied for PMI prediction of human cadavers. Accurate estimates indicated a reproducible succession of gut microbes across rats and humans. Together these results suggest that microbial succession was predictable and can be developed into a forensic tool for estimating PMI.

Optimal cisplatin cycles in locally advanced cervical carcinoma patients treated with concurrent chemoradiotherapy.

PURPOSE: To analyze the effect of cisplatin cycles on the clinical outcomes of patients with locally advanced cervical cancer (LACC) treated with concurrent chemoradiotherapy (CCRT). METHODS: This study included 749 patients with LACC treated with CCRT between January 2011 and December 2015. A receiver operating characteristic (ROC) curve was used to analyze the optimal cut-off of cisplatin cycles in predicting clinical outcomes. Clinicopathological features of the patients were compared using the Chi-square test. Prognosis was assessed using log-rank tests and Cox proportional hazard models. Toxicities were compared among different cisplatin cycle groups. RESULTS: Based on the ROC curve, the optimal cut-off of the cisplatin cycles was 4.5 (sensitivity, 64.3%; specificity, 54.3%). The 3-year overall, disease-free, loco-regional relapse-free, and distant metastasis-free survival for patients with low-cycles (cisplatin cycles < 5) and high-cycles (≥ 5) were 81.5% and 89.0% (P < 0.001), 73.4% and 80.1% (P = 0.024), 83.0% and 90.8% (P = 0.005), and 84.9% and 86.8% (P = 0.271), respectively. In multivariate analysis, cisplatin cycles were an independent prognostic factor for overall survival. In the subgroup analysis of high-cycle patients, patients who received over five cisplatin cycles had similar overall, disease-free, loco-regional relapse-free, and distant metastasis-free survival to patients treated with five cycles. Acute and late toxicities were not different between the two groups. CONCLUSION: Cisplatin cycles were associated with overall, disease-free, and loco-regional relapse-free survival in LACC patients who received CCRT. Five cycles appeared to be the optimal number of cisplatin cycles during CCRT.

Qualitative study on diabetic cutaneous wound healing with radiation crosslinked bilayer collagen scaffold in rat model.

Diabetes may leave patients more prone to skin problems, and minor skin conditions can more easily turn into serious damage to the extracellular matrix, which further impairs the skin's mechanical properties and delays wound healing. Therefore, the aim of the work is to develop extracellular matrix substitution to remodel the mechanical properties of diabetic cutaneous wound and thus accelerate diabetic wound healing. A green fabrication approach was used to prepare radiation crosslinked bilayer collagen scaffold from collagen dispersion. The morphological, mechanical and swelling characteristics of radiation crosslinked bilayer collagen scaffold were assessed to be suitable for cutaneous wound remodeling. The feasibility of radiation crosslinked bilayer collagen scaffold was performed on full-skin defect of streptozotocin-induced diabetic rats. The tissue specimens were harvested after 7, 14, and 21 days. Histopathological analysis showed that radiation crosslinked bilayer collagen scaffold has beneficial effects on inducing skin regeneration and remodeling in diabetic rats. In addition, immunohistochemical staining further revealed that the radiation crosslinked bilayer collagen scaffold could not only significantly accelerate the diabetic wound healing, but also promote angiogenesis factor (CD31) production. Vascularization was observed as early as day 7. The work expands the therapeutic ideas for cutaneous wound healing in diabetes.

Corrigendum: A progression analysis of motor features in Parkinson's disease based on the mapper algorithm.

[This corrects the article DOI: 10.3389/fnagi.2023.1047017.].

Specific Distribution of Digital Gait Biomarkers in Parkinson's Disease Using Body-Worn Sensors and Machine Learning.

Gait impairment leads to reduced social activities and low quality of life in people with Parkinson's disease (PD). PD is associated with unique gait signs and distributions of gait features. The assessment of gait characteristics is crucial in the diagnosis and treatment of PD. At present, the number and distribution of gait features associated with different PD stages are not clear. Here, we used whole-body multinode wearable devices combined with machine learning to build a classification model of early PD (EPD) and mild PD (MPD). Our model exhibited significantly improved accuracy for the EPD and MPD groups compared with the healthy control (HC) group (EPD vs HC accuracy = 0.88, kappa = 0.75, AUC = 0.88; MPD vs HC accuracy = 0.94, kappa = 0.84, AUC = 0.90). Furthermore, the distribution of gait features was distinguishable among the HC, EPD, and MPD groups (EPD based on variability features [40%]; MPD based on amplitude features [30%]). Here, we showed promising gait models for PD classification and provided reliable gait features for distinguishing different PD stages. Further multicenter clinical studies are needed to generalize the findings.

Scale-invariant sensitivity for multi-purpose water reservoirs management with temporal scale-dependent modeling.

High-resolution temporal data (e.g., daily) is valuable for the decision-making of water resources management because it more accurately captures fine-scale processes and extremes than the coarse temporal data (e.g., weekly or monthly). However, many studies rarely consider this superior suitability for water resource modeling and management; instead, they often use whichever data is more readily available. So far, no comparative investigations have been conducted to determine if access to different time-scale data would change decision-maker perceptions or the rationality of decision making. This study proposes a framework for assessing the impact of different temporal scales on water resource management and the performance objective's sensitivity to uncertainties. We built the multi-objective operation models and operating rules of a water reservoir system based on daily, weekly, and monthly scales, respectively, using an evolution multi-objective direct policy search. The temporal scales of the input variables (i.e., streamflow) affect both the model structures and the output variables. In exploring these effects, we reevaluated the temporal scale-dependent operating rules under uncertain streamflow sets generated from synthetic hydrology. Finally, we obtained the output variable's sensitivities to the uncertain factors at different temporal scales using the distribution-based sensitivity analysis method. Our results show that water management based on too coarse resolution might give decision makers the wrong perception because the effect of actual extreme streamflow process on the performance objectives is ignored. The streamflow uncertainty is more influential than the uncertainty associated with operating rules. However, the sensitivities are characterized by temporal scale invariance, as the differences of the sensitivity between different temporal scales are not obvious over the uncertainties in streamflow and thresholds. These results show that water management should consider the resolution-dependent effect of temporal scales for balancing modeling complexity and computational cost.

Remote scoring models of rigidity and postural stability of Parkinson's disease based on indirect motions and a low-cost RGB algorithm.

Background and objectives: The Movement Disorder Society's Unified Parkinson's Disease Rating Scale Part III (MDS-UPDRS III) is mostly common used for assessing the motor symptoms of Parkinson's disease (PD). In remote circumstances, vision-based techniques have many strengths over wearable sensors. However, rigidity (item 3.3) and postural stability (item 3.12) in the MDS-UPDRS III cannot be assessed remotely since participants need to be touched by a trained examiner during testing. We developed the four scoring models of rigidity of the neck, rigidity of the lower extremities, rigidity of the upper extremities, and postural stability based on features extracted from other available and touchless motions. Methods: The red, green, and blue (RGB) computer vision algorithm and machine learning were combined with other available motions from the MDS-UPDRS III evaluation. A total of 104 patients with PD were split into a train set (89 individuals) and a test set (15 individuals). The light gradient boosting machine (LightGBM) multiclassification model was trained. Weighted kappa (k), absolute accuracy (ACC ± 0), and Spearman's correlation coefficient (rho) were used to evaluate the performance of model. Results: For model of rigidity of the upper extremities, k = 0.58 (moderate), ACC ± 0 = 0.73, and rho = 0.64 (moderate). For model of rigidity of the lower extremities, k = 0.66 (substantial), ACC ± 0 = 0.70, and rho = 0.76 (strong). For model of rigidity of the neck, k = 0.60 (moderate), ACC ± 0 = 0.73, and rho = 0.60 (moderate). For model of postural stability, k = 0.66 (substantial), ACC ± 0 = 0.73, and rho = 0.68 (moderate). Conclusion: Our study can be meaningful for remote assessments, especially when people have to maintain social distance, e.g., in situations such as the coronavirus disease-2019 (COVID-19) pandemic.

A progression analysis of motor features in Parkinson's disease based on the mapper algorithm.

Background: Parkinson's disease (PD) is a neurodegenerative disease with a broad spectrum of motor and non-motor symptoms. The great heterogeneity of clinical symptoms, biomarkers, and neuroimaging and lack of reliable progression markers present a significant challenge in predicting disease progression and prognoses. Methods: We propose a new approach to disease progression analysis based on the mapper algorithm, a tool from topological data analysis. In this paper, we apply this method to the data from the Parkinson's Progression Markers Initiative (PPMI). We then construct a Markov chain on the mapper output graphs. Results: The resulting progression model yields a quantitative comparison of patients' disease progression under different usage of medications. We also obtain an algorithm to predict patients' UPDRS III scores. Conclusions: By using mapper algorithm and routinely gathered clinical assessments, we developed a new dynamic models to predict the following year's motor progression in the early stage of PD. The use of this model can predict motor evaluations at the individual level, assisting clinicians to adjust intervention strategy for each patient and identifying at-risk patients for future disease-modifying therapy clinical trials.

Ga3+ loaded radiation crosslinked gel-Alg-CMC hydrogels for promoting diabetic wound healing.

The metal gallium holds great promise in the fight against infection by disrupting bacterial iron metabolism through a "Trojan horse" technique. It is well worth trying to investigate the potential for gallium-mediated hydrogels for the treatment of infected wounds. In this paper, Ga3+ is innovatively given an important role in hydrogels based on the conventional multi-component hydrogel with metal ion binding gelation strategy. Thus, Ga@Gel-Alg-CMCs hydrogel with broad-spectrum antimicrobial activity is reported on the treatment of infected wounds. The morphology, degradability, and swelling behavior together indicated the excellent physical properties of this hydrogel. Interestingly, in vivo results also showed favorable biocompatibility, slowing down wound infection and promoting diabetic wound healing, making the gallium-doped hydrogel an ideal antimicrobial dressing.

Wearable sensor-based gait analysis to discriminate early Parkinson's disease from essential tremor.

BACKGROUND: Differentiating early-stage Parkinson's disease (PD) from essential tremor (ET) is challenging since they have some overlapping clinical features. Since early-stage PD may present with slight gait impairment and ET generally does not, gait analysis could be used to differentiate PD from ET using machine learning. OBJECTIVE: To differentiate early-stage PD from ET via machine learning using gait and postural transition parameters calculated using the raw kinematic signal captured from inertial measurement unit (IMU) sensors. METHODS: Gait and postural transition parameters were collected from 84 early-stage PD and 80 ET subjects during the Time Up and Go (TUG) test. We randomly split our data into training and test data. Within the training data, we separated the TUG test into four components: standing, straight walk, turning, and sitting to build weighted average ensemble classification models. The four components' weight indices were trained using logistic regression. Several ensemble models' leave-one-out cross-validation (LOOCV) performances were compared. Independent test data were used to evaluate the model with the best LOOCV performance. RESULTS: The best weighted average ensemble classification model LOOCV results included an accuracy of 84%, Kappa of 0.68, sensitivity of 85.9%, specificity of 82.1%, and AUC of 0.912. Thirty-three gait and postural transition parameters, such as Arm-Symbolic Symmetry Index and 180° Turn-Max Angular Velocity, were included in Feature Group III. The independent test data achieved a 75.8% accuracy. CONCLUSIONS: Our findings suggest that gait and postural transition parameters obtained from wearable sensors combined with machine learning had the potential to distinguish between early-stage PD and ET.

Novel Prediction Method Applied to Wound Age Estimation: Developing a Stacking Ensemble Model to Improve Predictive Performance Based on Multi-mRNA.

(1) Background: Accurate diagnosis of wound age is crucial for investigating violent cases in forensic practice. However, effective biomarkers and forecast methods are lacking. (2) Methods: Samples were collected from rats divided randomly into control and contusion groups at 0, 4, 8, 12, 16, 20, and 24 h post-injury. The characteristics of concern were nine mRNA expression levels. Internal validation data were used to train different machine learning algorithms, namely random forest (RF), support vector machine (SVM), multilayer perceptron (MLP), gradient boosting (GB), and stochastic gradient descent (SGD), to predict wound age. These models were considered the base learners, which were then applied to developing 26 stacking ensemble models combining two, three, four, or five base learners. The best-performing stacking model and base learner were evaluated through external validation data. (3) Results: The best results were obtained using a stacking model of RF + SVM + MLP (accuracy = 92.85%, area under the receiver operating characteristic curve (AUROC) = 0.93, root-mean-square-error (RMSE) = 1.06 h). The wound age prediction performance of the stacking models was also confirmed for another independent dataset. (4) Conclusions: We illustrate that machine learning techniques, especially ensemble algorithms, have a high potential to be used to predict wound age. According to the results, the strategy can be applied to other types of forensic forecasts.