Metformin mitigates adipogenesis of fibro-adipogenic progenitors after rotator cuff tears via activating mTOR/ULK1-mediated autophagy.

Muscular fatty infiltration is a common issue after rotator cuff tears (RCTs), which impair shoulder function. Females suffer a higher prevalence and a more severe degree of muscular fatty infiltration after RCT when compared with males, with the underlying mechanisms remaining unclear. Fibro-adipogenic progenitors (FAPs) are the primary source of muscular fatty infiltration following RCT. Our findings disclose that gender-specific disparities in muscular fatty infiltration are linked to mTOR/ULK1-mediated autophagy of FAPs. Decreased autophagic activity contributes to adipogenic differentiation in female FAPs after RCT. Furthermore, metformin could enhance mTOR/ULK1-mediated autophagic processes of FAPs, thereby alleviating fatty infiltration and improving shoulder functionality after RCT. Together, our study reveals that gender differences in muscular fatty infiltration arise from distinct autophagic activities. Metformin could be a promising noninvasive intervention to ameliorate muscular fatty infiltration of RCT.NEW & NOTEWORTHY The current study demonstrated that gender-specific disparities in muscular fatty infiltration are attributed to mTOR/ULK1-mediated autophagy of FAPs. Decreased autophagic activity contributes to adipogenic differentiation in female FAPs after RCT. Moreover, metformin could enhance mTOR/ULK1-mediated autophagic processes of FAPs, thereby alleviating fatty infiltration and improving shoulder functionality after RCT. Therefore, metformin could be a promising noninvasive intervention to ameliorate muscular fatty infiltration of RCT.

Multi-Branching Temporal Convolutional Network With Tensor Data Completion for Diabetic Retinopathy Prediction.

Diabetic retinopathy (DR), a microvascular complication of diabetes, is the leading cause of vision loss among working-aged adults. However, due to the low compliance rate of DR screening and expensive medical devices for ophthalmic exams, many DR patients did not seek proper medical attention until DR develops to irreversible stages (i.e., vision loss). Fortunately, the widely available electronic health record (EHR) databases provide an unprecedented opportunity to develop cost-effective machine-learning tools for DR detection. This paper proposes a Multi-branching Temporal Convolutional Network with Tensor Data Completion (MB-TCN-TC) model to analyze the longitudinal EHRs collected from diabetic patients for DR prediction. Experimental results demonstrate that the proposed MB-TCN-TC model not only effectively copes with the imbalanced data and missing value issues commonly seen in EHR datasets but also captures the temporal correlation and complicated interactions among medical variables in the longitudinal clinical records, yielding superior prediction performance compared to existing methods. Specifically, our MB-TCN-TC model provides AUROC and AUPRC scores of 0.949 and 0.793 respectively, achieving an improvement of 6.27% on AUROC, 11.85% on AUPRC, and 19.3% on F1 score compared with the traditional TCN model.

The Phylogeny and Metabolic Potentials of a Lignocellulosic Material-Degrading Aliiglaciecola Bacterium Isolated from Intertidal Seawater in East China Sea.

Lignocellulosic materials are composed of cellulose, hemicellulose and lignin and are one of the most abundant biopolymers in marine environments. The extent of the involvement of marine microorganisms in lignin degradation and their contribution to the oceanic carbon cycle remains elusive. In this study, a novel lignin-degrading bacterial strain, LCG003, was isolated from intertidal seawater in Lu Chao Harbor, East China Sea. Phylogenetically, strain LCG003 was affiliated with the genus Aliiglaciecola within the family Alteromonadaceae. Metabolically, strain LCG003 contains various extracellular (signal-fused) glycoside hydrolase genes and carbohydrate transporter genes and can grow with various carbohydrates as the sole carbon source, including glucose, fructose, sucrose, rhamnose, maltose, stachyose and cellulose. Moreover, strain LCG003 contains many genes of amino acid and oligopeptide transporters and extracellular peptidases and can grow with peptone as the sole carbon and nitrogen source, indicating a proteolytic lifestyle. Notably, strain LCG003 contains a gene of dyp-type peroxidase and strain-specific genes involved in the degradation of 4-hydroxy-benzoate and vanillate. We further confirmed that it can decolorize aniline blue and grow with lignin as the sole carbon source. Our results indicate that the Aliiglaciecola species can depolymerize and mineralize lignocellulosic materials and potentially play an important role in the marine carbon cycle.

Rapid observation of dispersion curves in system-on-chip devices.

We have developed a rapid detection instrument to characterize the behavior of light on the surface of devices during light-matter interactions. The equipment enables the non-destructive and real-time observations of the dispersion curves for microstructures, providing the basis for a large number of new planar photonic chip applications. The method is based on the traditional prismatic reflection and makes full use of the grating dispersion capabilities, enabling simultaneous multi-wavelength and multi-angle reflectance measurements over a wide range. This method is beneficial for designing new microstructure devices and brings convenience to delicate microstructure processing. The instruments do not require any mechanical scanning, allowing for rapid acquisition, and the integrated and reusable optics make them easily miniaturized. Additionally, the functionalized design allows for spectral analysis applications, such as far-field spectral measurements. The instrument can also be easily integrated into established microscopic imaging systems, extending their observational characterization capabilities as well as accomplishing dynamic monitoring in proven system-on-a-chip devices.

Urban digital economy, environmental pollution, and resident's health-empirical evidence from China.

In light of China's rapid advancement in the digital economy and the implementation of the "Healthy China" initiative, it is crucial to assess the impact of the digital economy on residents' health. This study analyzes data from the 2012, 2014, and 2016 China Labor Force Dynamics Survey (CLDS) to evaluate the health of residents using both subjective and objective criteria. Furthermore, it calculates the digital economy development index for Chinese cities and investigates its influence on the subjective and objective health of residents, along with the underlying mechanisms. The empirical results reveal a U-shaped pattern in the effect of the digital economy on health levels, initially detrimental but subsequently beneficial. The analysis of mechanisms shows that the digital economy's development initially increases and then decreases environmental pollution, impacting health through environmental changes. Additionally, the study finds variations in this impact based on age and urban-rural differences, with more pronounced effects on rural and older adult populations, who also experience the U-shaped curve's turning point more rapidly. These findings highlight the necessity of advancing digital economy infrastructure to positively influence environmental quality and improve public health. The study emphasizes the urgent need for policymakers to invest in digital infrastructure to foster a sustainable and healthy future. This requires a holistic approach to development, focusing on both urban and rural areas, to promote inclusive growth and reduce the digital divide.

Skin-Integrated Electrodes Based on Room-Temperature Curable, Highly Conductive Silver/Polydimethylsiloxane Composites.

The quality of electrophysiological (EP) signals heavily relies on the electrode's contact with the skin. However, motion or exposure to water can easily destabilize this connection. In contrast to traditional methods of attaching electrodes to the skin surface, this study introduces a skin-integration strategy inspired by the skin's intergrown structure. A highly conductive and room-temperature curable composite composed of silver microflakes and polydimethylsiloxane (Ag/PDMS) is applied to the skin. Before curing, the PDMS oil partially diffuse into the stratum corneum (SC) layer of the skin. Upon curing, the composite solidifies into an electrode that seamlessly integrated with the skin, resembling a natural extension. This skin-integration strategy offers several advantages. It minimizes motion artifacts resulting from relative electrode-skin displacement, significantly reduces interface impedance (67% of commercial Ag/AgCl gel electrodes at 100 Hz) and withstands water flushes due to its hydrophobic nature. These advantages pave the way for promising advancements in EP signal recording, particularly during motion and underwater conditions.

Hierarchical deep learning with Generative Adversarial Network for automatic cardiac diagnosis from ECG signals.

Cardiac disease is the leading cause of death in the US. Accurate heart disease detection is critical to timely medical treatment to save patients' lives. Routine use of the electrocardiogram (ECG) is the most common method for physicians to assess the cardiac electrical activities and detect possible abnormal conditions. Fully utilizing the ECG data for reliable heart disease detection depends on developing effective analytical models. In this paper, we propose a two-level hierarchical deep learning framework with Generative Adversarial Network (GAN) for ECG signal analysis. The first-level model is composed of a Memory-Augmented Deep AutoEncoder with GAN (MadeGAN), which aims to differentiate abnormal signals from normal ECGs for anomaly detection. The second-level learning aims at robust multi-class classification for different arrhythmia identification, which is achieved by integrating the transfer learning technique to transfer knowledge from the first-level learning with the multi-branching architecture to handle the data-lacking and imbalanced data issues. We evaluate the performance of the proposed framework using real-world ECG data from the MIT-BIH arrhythmia database. Experimental results show that our proposed model outperforms existing methods that are commonly used in current practice.

Joint torques estimation in human gait based on Gaussian process.

BACKGROUND: Human gait involves activities in nervous and musculoskeletal dynamics to modulate joint torques with time continuously for adapting to varieties of walking conditions. OBJECTIVE: The goal of this paper is to estimate the joint torques of lower limbs in human gait based on Gaussian process. METHOD: The potential uses of this study include optimization of exoskeleton assistance, control of the active prostheses, and modulating the joint torque for human-like robots. To achieve this, Gaussian process (GP) based data fusion algorithm is established with joint angles as the inputs. RESULTS: The statistic nature of the proposed model can explore the correlations between joint angles and joint torques, and enable accurate joint-torque estimations. Experiments were conducted for 5 subjects at three walking speed (0.8 m/s, 1.2 m/s, 1.6 m/s). CONCLUSION: The results show that it is possible to estimate the joint torques at different scenarios.

A Miniaturized Piezoelectric MEMS Accelerometer with Polygon Topological Cantilever Structure.

This work proposes a miniaturized piezoelectric MEMS accelerometer based on polygonal topology with an area of only 868 × 833 µm2. The device consists of six trapezoidal cantilever beams with shorter fixed sides. Meanwhile, a device with larger fixed sides is also designed for comparison. The theoretical and finite element models are established to analyze the effect of the beam's effective stiffness on the output voltage and natural frequency. As the stiffness of the device decreases, the natural frequency of the device decreases while the output signal increases. The proposed polygonal topology with shorter fixed sides has higher voltage sensitivity than the larger fixed one based on finite element simulations. The piezoelectric accelerometers are fabricated using Cavity-SOI substrates with a core piezoelectric film of aluminum nitride (AlN) of about 928 nm. The fabricated piezoelectric MEMS accelerometers have good linearity (0.99996) at accelerations less than 2 g. The measured natural frequency of the accelerometer with shorter fixed sides is 98 kHz, and the sensitivity, resolution, and minimum detectable signal at 400 Hz are 1.553 mV/g, 1 mg, and 2 mg, respectively. Compared with the traditional trapezoidal cantilever with the same diaphragm area, its output voltage sensitivity is increased by 22.48%.

Deep learning for non-parameterized MEMS structural design.

The geometric designs of MEMS devices can profoundly impact their physical properties and eventual performances. However, it is challenging for researchers to rationally consider a large number of possible designs, as it would be very time- and resource-consuming to study all these cases using numerical simulation. In this paper, we report the use of deep learning techniques to accelerate the MEMS design cycle by quickly and accurately predicting the physical properties of numerous design candidates with vastly different geometric features. Design candidates are represented in a nonparameterized, topologically unconstrained form using pixelated black-and-white images. After sufficient training, a deep neural network can quickly calculate the physical properties of interest with good accuracy without using conventional numerical tools such as finite element analysis. As an example, we apply our deep learning approach in the prediction of the modal frequency and quality factor of disk-shaped microscale resonators. With reasonable training, our deep learning neural network becomes a high-speed, high-accuracy calculator: it can identify the flexural mode frequency and the quality factor 4.6 × 103 times and 2.6 × 104 times faster, respectively, than conventional numerical simulation packages, with good accuracies of 98.8 ± 1.6% and 96.8 ± 3.1%, respectively. When simultaneously predicting the frequency and the quality factor, up to ~96.0% of the total computation time can be saved during the design process. The proposed technique can rapidly screen over thousands of design candidates and promotes experience-free and data-driven MEMS structural designs.

Physiological and multi-omics responses of Neoporphyra haitanensis to dehydration-rehydration cycles.

BACKGROUND: Seaweeds in the upper intertidal zone experience extreme desiccation during low tide, followed by rapid rehydration during high tide. Porphyra sensu lato are typical upper intertidal seaweeds. Therefore, it is valuable to investigate the adaptive mechanisms of seaweed in response to dehydration-rehydration stress. RESULTS: A reduction in photosynthetic capacity and cell shrinkage were observed when N. haitanensis was dehydrated, and such changes were ameliorated once rehydrated. And the rate and extent of rehydration were affected by the air flow speed, water content before rehydration, and storage temperature and time. Rapid dehydration at high air-flow speed and storage at - 20 °C with water content of 10% caused less damage to N. haitanensis and better-protected cell activity. Moreover, proteomic and metabolomic analyses revealed the abundance members of the differentially expressed proteins (DEPs) and differentially abundant metabolites (DAMs) mainly involved in antioxidant system and osmotic regulation. The ascorbic acid-glutathione coupled with polyamine antioxidant system was enhanced in the dehydration response of N. haitanensis. The increased soluble sugar content, the accumulated polyols, but hardly changed (iso)floridoside and insignificant amount of sucrose during dehydration indicated that polyols as energetically cheaper organic osmolytes might help resist desiccation. Interestingly, the recovery of DAMs and DEPs upon rehydration was fast. CONCLUSIONS: Our research results revealed that rapid dehydration and storage at - 20 °C were beneficial for recovery of N. haitanensis. And the strategy to resist dehydration was strongly directed toward antioxidant activation and osmotic regulation. This work provided valuable insights into physiological changes and adaptative mechanism in desiccation, which can be applied for seaweed farming.

Insights into the Ancient Adaptation to Intertidal Environments by Red Algae Based on a Genomic and Multiomics Investigation of Neoporphyra haitanensis.

Colonization of land from marine environments was a major transition for biological life on Earth, and intertidal adaptation was a key evolutionary event in the transition from marine- to land-based lifestyles. Multicellular intertidal red algae exhibit the earliest, systematic, and successful adaptation to intertidal environments, with Porphyra sensu lato (Bangiales, Rhodophyta) being a typical example. Here, a chromosome-level 49.67 Mb genome for Neoporphyra haitanensis comprising 9,496 gene loci is described based on metagenome-Hi-C-assisted whole-genome assembly, which allowed the isolation of epiphytic bacterial genome sequences from a seaweed genome for the first time. The compact, function-rich N. haitanensis genome revealed that ancestral lineages of red algae share common horizontal gene transfer events and close relationships with epiphytic bacterial populations. Specifically, the ancestor of N. haitanensis obtained unique lipoxygenase family genes from bacteria for complex chemical defense, carbonic anhydrases for survival in shell-borne conchocelis lifestyle stages, and numerous genes involved in stress tolerance. Combined proteomic, transcriptomic, and metabolomic analyses revealed complex regulation of rapid responses to intertidal dehydration/rehydration cycling within N. haitanensis. These adaptations include rapid regulation of its photosynthetic system, a readily available capacity to utilize ribosomal stores, increased methylation activity to rapidly synthesize proteins, and a strong anti-oxidation system to dissipate excess redox energy upon exposure to air. These novel insights into the unique adaptations of red algae to intertidal lifestyles inform our understanding of adaptations to intertidal ecosystems and the unique evolutionary steps required for intertidal colonization by biological life.

Multi-Branching Temporal Convolutional Network for Sepsis Prediction.

Sepsisis among the leading causes of morbidity and mortality in modern intensive care units. Accurate sepsis prediction is of critical importance to save lives and reduce medical costs. The rapid advancements in sensing and information technology facilitate the effective monitoring of patients' health conditions, generating a wealth of medical data, and provide an unprecedented opportunity for data-driven diagnosis of sepsis. However, real-world medical data are often complexly structured with a high level of uncertainty (e.g., missing values, imbalanced data). Realizing the full data potential depends on developing effective analytical models. In this paper, we propose a novel predictive framework with Multi-Branching Temporal Convolutional Network (MB-TCN) to model the complexly structured medical data for robust prediction of sepsis. The MB-TCN framework not only efficiently handles the missing value and imbalanced data issues but also effectively captures the temporal pattern and heterogeneous variable interactions. We evaluate the performance of the proposed MB-TCN in predicting sepsis using real-world medical data from PhysioNet/Computing in Cardiology Challenge 2019. Experimental results show that MB-TCN outperforms existing methods that are commonly used in current practice.

Successful treatment of acute circulatory failure of unknown cause using critical ultrasound-guided reverse fluid resuscitation: A case report.

RATIONALE: Fluid resuscitation manages shock effectively. However, shock is not always caused by hypovolemia; various types of shock have variable volumetric reactivity. Combined echocardiography and lung ultrasound (LUS) is a new technique for assessing volume status and pulmonary edema in these patients. We report a case of unexplained acute circulatory failure and acute kidney injury (AKI) aggravated by active fluid resuscitation. We used the critical consultation ultrasonic examination (CCUE) protocol for evaluation, and successfully revived the patient with reverse fluid resuscitation. PATIENT CONCERNS: An 82-year-old man with hypertension, atrial fibrillation, and left ventricular diastolic dysfunction (LVDD) was admitted with abdominal distention and lower extremity edema. He developed symptoms of acute circulatory failure, including low blood pressure, anuria, and skin spots. After positive fluid resuscitation, the blood pressure lowered further, and moist rales were audible over both lungs. DIAGNOSIS: We performed bedside critical ultrasound for evaluation. The differential diagnoses based on the findings included left atrial and right heart dilatation, low cardiac output owing to reduced left ventricular ejection consequent to excessive circulatory capacity, right heart dilation, and left ventricular compression, and pulmonary edema caused by volume overload. INTERVENTIONS: Infusion was withheld, and tracheal intubation and mechanical ventilation were instituted to assist breathing; reverse fluid resuscitation was initiated, using continuous renal replacement therapy (CRRT) to maintain a negative fluid balance. OUTCOMES: Within 72 hours of fluid withdrawal, the blood pressure reverted to normal, symptoms of pulmonary edema were alleviated, and the circulation and tissue perfusion were restored. The symptoms of acute renal injury are relieved and allowing urine formation without support. LESSONS: Not all patients with acute circulatory failure require positive fluid resuscitation. Fluid balance should be closely monitored and managed. Potential intolerance to the rapid increase in volume may lead to biventricular interaction, ultimately leading to acute circulatory failure. The shock caused by volume overload should be treated with reverse fluid resuscitation. Combined echocardiography and LUS is a powerful tool for the differential diagnosis of circulatory and respiratory dysfunction.

Synergistical action of the ß2 adrenoceptor and fatty acid binding protein 2 polymorphisms on the loss of glomerular filtration rate in Chinese patients with type 2 diabetic nephropathy.

PURPOSE: Since altered sympathetic nerve activity and insulin resistance are implicated in the pathogenesis of type 2 diabetic nephropathy, we investigated the effect of polymorphic Arg16Gly and Gln27Glu in the ß2 adrenoceptor gene and Ala54Thr in the fatty acid binding protein 2 gene on the estimated glomerular filtration rate (eGFR) in Chinese patients with the above disease. METHODS: A total of 552 diabetic subjects recruited from annual health examinations were studied. The eGFR was calculated from the Modification of Diet in Renal Disease equation for the Chinese. Plasma norepinephrine level and genotype were determined by high-performance liquid chromatography-tandem mass spectrometry and TaqMan method, respectively. Holter-derived heart rate viability (HRV) and the MRI-generated renal apparent diffusion coefficient (ADC) were evaluated. RESULTS: The Gly16Gly and Thr54Thr homozygotes had significantly higher microalbuminuria and lower eGFR against other genotypes in their individual polymorphism. Besides, the Gly16Gly variant exhibited markedly elevated norepinephrine level, whereas indicative of insulin resistance was increased in the Thr54Thr one. Multiple linear regression analysis further confirmed the independent genetic effect on the eGFR. Moreover, multifactor dimensionality reduction method detected a gene-gene synergistic action that subjects with the Gly16Gly/Thr54Thr genotype were exposed to higher risk of eGFR loss. Finally, these findings were accompanied by lower HRV and ADC, indicating sympathetically mediated hemodynamic changes. CONCLUSIONS: By uncovering the genetic component of the coherent interplay between the elevated sympathetic nerve activity and metabolic disorders, our observations might promote the development of novel personalized prevention and management strategies against the diabetic nephropathy, especially in the genetically susceptible individuals.