Sampling Trade-Offs in Duty-Cycled Systems for Air Quality Low-Cost Sensors.

The use of low-cost sensors in conjunction with high-precision instrumentation for air pollution monitoring has shown promising results in recent years. One of the main challenges for these sensors has been the quality of their data, which is why the main efforts have focused on calibrating the sensors using machine learning techniques to improve the data quality. However, there is one aspect that has been overlooked, that is, these sensors are mounted on nodes that may have energy consumption restrictions if they are battery-powered. In this paper, we show the usual sensor data gathering process and we study the existing trade-offs between the sampling of such sensors, the quality of the sensor calibration, and the power consumption involved. To this end, we conduct experiments on prototype nodes measuring tropospheric ozone, nitrogen dioxide, and nitrogen monoxide at high frequency. The results show that the sensor sampling strategy directly affects the quality of the air pollution estimation and that each type of sensor may require different sampling strategies. In addition, duty cycles of 0.1 can be achieved when the sensors have response times in the order of two minutes, and duty cycles between 0.01 and 0.02 can be achieved when the sensor response times are negligible, calibrating with hourly reference values and maintaining a quality of calibrated data similar to when the node is connected to an uninterruptible power supply.

Alternative Evaluation to Earthworm Toxicity Test in Polychlorinated Biphenyls Spiked and Remediated Soils.

Polychlorinated biphenyls (PCBs) are a class of persistent organic pollutants that pose a threat to environment and human health. Aiming at predicting PCBs risk in actual soil ecosystem, this study was conducted by chemical and biological methods to assess the bioavailability of PCBs in spiked soil, and in field-contaminated soils before or after remediation. The three chemical methods were Soxhlet, n-butanol and hydroxypropyl-ß-cyclodextrin (HPCD). Results were compared to actual PCB bioaccumulation in earthworms (Eisenia fetida). HPCD extraction was the best to predict the actual PCB bioaccumulation in all soils. The results suggest that HPCD could be an effective alternative method to earthworm toxicity test. This study provides strategy to understand the toxicity assessment in contaminated soil and soil after remediation.

Endoscopic Central Lymph Node Dissection via Breast Combined with Oral Approach for Papillary Thyroid Carcinoma: A Preliminary Study.

OBJECTIVE: To investigate the feasibility and necessity of endoscopic thyroidectomy with central lymph node dissection via the combined breast and trans-oral approaches. METHODS: Six patients with papillary thyroid carcinoma who underwent endoscopic total thyroidectomy with central node dissection via combined breast and trans-oral approaches from November 2014 to January 2015 in Zhongshan Hospital, Xiamen University, were analyzed. RESULTS: After completion of endoscopic central lymph node dissection via the traditional breast approach, eight pieces of lymph nodes could still be dissected via the trans-oral approach. Two of these eight pieces were positive for thyroid cancer metastasis. CONCLUSIONS: It is advisable to perform endoscopic central lymph node dissection for thyroid carcinoma via the breast approach combined with the trans-oral approach.

Forecasting rheumatoid arthritis patient arrivals by including meteorological factors and air pollutants.

The burden of rheumatoid arthritis (RA) has gradually elevated, increasing the need for medical resource redistribution. Forecasting RA patient arrivals can be helpful in managing medical resources. However, no relevant studies have been conducted yet. This study aims to construct a long short-term memory (LSTM) model, a deep learning model recently developed for novel data processing, to forecast RA patient arrivals considering meteorological factors and air pollutants and compares this model with traditional methods. Data on RA patients, meteorological factors and air pollutants from 2015 to 2022 were collected and normalized to construct moving average (MA)- and autoregressive (AR)-based and LSTM models. After data normalization, the root mean square error (RMSE) was adopted to evaluate models' forecast ability. A total of 2422 individuals were enrolled. Not using the environmental data, the RMSEs of the MA- and AR-based models' test sets are 0.131, 0.132, and 0.117 when the training set: test set ratio is 2:1, 3:1, and 7:1, while they are 0.110, 0.130, and 0.112 for the univariate LSTM models. Considering meteorological factors and air pollutants, the RMSEs of the MA- and AR-based model test sets were 0.142, 0.303, and 0.164 when the training set: test set ratio is 2:1, 3:1, and 7:1, while they were 0.108, 0.119, and 0.109 for the multivariable LSTM models. Our study demonstrated that LSTM models can forecast RA patient arrivals more accurately than MA- and AR-based models for datasets of all three sizes. Considering the meteorological factors and air pollutants can further improve the forecasting ability of the LSTM models. This novel method provides valuable information for medical management, the optimization of medical resource redistribution, and the alleviation of resource shortages.

Continuation of immunotherapy beyond progression is beneficial to the survival of advanced non-small-cell lung cancer.

PURPOSE: To investigate the potential clinical importance of continuing immunotherapy beyond progression in patients with advanced non-small-cell lung cancer (aNSCLC). METHODS: The data of patients with aNSCLC who experienced progressive disease after receiving first-line immunotherapy plus chemotherapy were collected from multiple centers for the period from January 1, 2018 to May 31, 2022. According to the second-line treatment, the patients were classified into two groups: the continuation of immunotherapy beyond progression (CIBP) group and the discontinuation of immunotherapy beyond progression (DIBP) group. The efficacy and safety of the treatment were compared between the groups. RESULTS: Overall, data from 169 patients were analyzed; 93 patients were enrolled in the CIBP group and 76 patients were in the DIBP group. The median second-line progression-free survival was 5.5 months in the CIBP group, which for the DIBP group was 3.4 (p = 0.011). The median overall survival of the CIBP group was 13.3 months, whereas that of the DIBP group was 8.8 months (p = 0.031). The disease control rate of the CIBP group (79.57%) was observably higher than that of the DIBP group (64.47%; p = 0.028). Among patients who responded better (complete or partial response) to prior therapy, the median progression-free survival was 5.5 months and 3.3 months in the CIBP and DIBP groups respectively (p = 0.022), and the median overall survival was 14.8 months and 8.8 months in the CIBP and DIBP groups respectively (p = 0.046). CONCLUSIONS: Continuing immunotherapy as a second-line treatment could be beneficial to the survival of patients with aNSCLC with disease progression beyond initial chemotherapy combined with immunotherapy.

Myosin Light Chain Phosphatase Plays an Important Role in Cardiac Fibrosis in a Model of Mineralocorticoid Receptor-Associated Hypertension.

BACKGROUND: Myosin phosphatase targeting subunit 2 (MYPT2) is an important subunit of cardiac MLC (myosin light chain) phosphatase, which plays a crucial role in regulating the phosphorylation of MLC to phospho-MLC (p-MLC). A recent study demonstrated mineralocorticoid receptor-related hypertension is associated with RhoA/Rho-associated kinase/MYPT1 signaling upregulation in smooth muscle cells. Our purpose is to investigate the effect of MYPT2 on cardiac function and fibrosis in mineralocorticoid receptor-related hypertension. METHODS AND RESULTS: HL-1 murine cardiomyocytes were incubated with different concentrations or durations of aldosterone. After 24-hour stimulation, aldosterone increased CTGF (connective tissue growth factor) and MYPT2 and decreased p-MLC in a dose-dependent manner. MYPT2 knockdown decreased CTGF. Cardiac-specific MYPT2-knockout (c-MYPT2-/-) mice exhibited decreased type 1 phosphatase catalytic subunit ß and increased p-MLC. A disease model of mouse was induced by subcutaneous aldosterone and 8% NaCl food for 4 weeks after uninephrectomy. Blood pressure elevation and left ventricular hypertrophy were observed in both c-MYPT2-/- and MYPT2+/+ mice, with no difference in heart weights or nuclear localization of mineralocorticoid receptor in cardiomyocytes. However, c-MYPT2-/- mice had higher ejection fraction and fractional shortening on echocardiography after aldosterone treatment. Histopathology revealed less fibrosis, reduced CTGF, and increased p-MLC in c-MYPT2-/- mice. Basal global radial strain and global longitudinal strain were higher in c-MYPT2-/- than in MYPT2+/+ mice. After aldosterone treatment, both global radial strain and global longitudinal strain remained higher in c-MYPT2-/- mice compared with MYPT2+/+ mice. CONCLUSIONS: Cardiac-specific MYPT2 knockout leads to decreased myosin light chain phosphatase and increased p-MLC. MYPT2 deletion prevented cardiac fibrosis and dysfunction in a model of mineralocorticoid receptor-associated hypertension.

Brain-computer Interaction in the Smart Era.

The brain-computer interface (BCI) system serves as a critical link between external output devices and the human brain. A monitored object's mental state, sensory cognition, and even higher cognition are reflected in its electroencephalography (EEG) signal. Nevertheless, unprocessed EEG signals are frequently contaminated with a variety of artifacts, rendering the analysis and elimination of impurities from the collected EEG data exceedingly challenging, not to mention the manual adjustment thereof. Over the last few decades, the rapid advancement of artificial intelligence (AI) technology has contributed to the development of BCI technology. Algorithms derived from AI and machine learning have significantly enhanced the ability to analyze and process EEG electrical signals, thereby expanding the range of potential interactions between the human brain and computers. As a result, the present BCI technology with the help of AI can assist physicians in gaining a more comprehensive understanding of their patients' physical and psychological status, thereby contributing to improvements in their health and quality of life.

Enhancing Orthopedic Knowledge Assessments: The Performance of Specialized Generative Language Model Optimization.

OBJECTIVE: This study aimed to evaluate and compare the effectiveness of knowledge base-optimized and unoptimized large language models (LLMs) in the field of orthopedics to explore optimization strategies for the application of LLMs in specific fields. METHODS: This research constructed a specialized knowledge base using clinical guidelines from the American Academy of Orthopaedic Surgeons (AAOS) and authoritative orthopedic publications. A total of 30 orthopedic-related questions covering aspects such as anatomical knowledge, disease diagnosis, fracture classification, treatment options, and surgical techniques were input into both the knowledge base-optimized and unoptimized versions of the GPT-4, ChatGLM, and Spark LLM, with their generated responses recorded. The overall quality, accuracy, and comprehensiveness of these responses were evaluated by 3 experienced orthopedic surgeons. RESULTS: Compared with their unoptimized LLMs, the optimized version of GPT-4 showed improvements of 15.3% in overall quality, 12.5% in accuracy, and 12.8% in comprehensiveness; ChatGLM showed improvements of 24.8%, 16.1%, and 19.6%, respectively; and Spark LLM showed improvements of 6.5%, 14.5%, and 24.7%, respectively. CONCLUSION: The optimization of knowledge bases significantly enhances the quality, accuracy, and comprehensiveness of the responses provided by the 3 models in the orthopedic field. Therefore, knowledge base optimization is an effective method for improving the performance of LLMs in specific fields.

Non-intrusive speech quality assessment with attention-based ResNet-BiLSTM.

Speech quality is frequently affected by a variety factors in online conferencing applications, such as background noise, reverberation, packet loss and network jitter. In real scenarios, it is impossible to obtain a clean reference signal for evaluating the quality of the conferencing speech. Therefore, an effective non-intrusive speech quality assessment (NISQA) method is necessary. In this paper, we propose a new network framework for NISQA based on ResNet and BiLSTM. ResNet is utilized to extract local features, while BiLSTM is used to integrate representative features with long-term time dependencies and sequential characteristics. Considering that ResNet may result in the loss of context information when applied to the NISQA task, we propose a variant of ResNet which can preserve the time series information of the conferencing speech. The experimental results demonstrate that the proposed method has a high correlation with the mean opinion score of clean, noisy and processed speech.

Usability of Serum Stanniocalcin-1 as a Prognostic Biochemical Marker of Acute Supratentorial Intracerebral Hemorrhage: A Prospective Cohort Study.

Objective: Stanniocalcin-1 (STC1) may be neuroprotective. This study aimed to evaluate the prognostic role of serum STC1 levels in intracerebral hemorrhage (ICH). Methods: This prospective observational study was assigned in two parts. In the first part, blood samples of 48 patients with ICH were acquired on admission and on days 1, 2, 3, 5, and 7 after ICH, and those of 48 controls were collected at their entry into the study. In the second part, blood samples of 141 patients with ICH were obtained upon admission. Serum STC1 levels were measured, and the National Institutes of Health Stroke Scale (NIHSS), hematoma volume, and poststroke 6-month modified Rankin Scale (mRS) scores were recorded. Dynamic changes in serum STC levels and their correlation with disease severity and prognosis were investigated. Results: Serum STC1 levels were elevated after ICH, peaked on day 1, plateaued on day 2, declined gradually afterwards, and were significantly higher than those in controls. Serum STC1 levels were independently correlated with NIHSS scores, hematoma volume, and the 6-month post-injury mRS scores. Serum STC1 levels, NIHSS scores, and hematoma volume independently predicted a poor prognosis (mRS scores of 3-6). The model integrating serum STC1 levels, NIHSS scores, and hematoma volume was visually displayed using a nomogram and was relatively stable using the Hosmer-Lemeshow test and calibration curve analysis. Under the receiver operating characteristic curve, serum STC1 levels efficiently predicted a poor prognosis and showed similar prognostic ability to NIHSS scores and hematoma volume. The preceding model had significantly higher prognostic capability than NIHSS scores and hematoma volume alone and their combination. Conclusion: Substantial enhancement of serum STC1 levels after ICH, which is strongly correlated with severity, independently distinguished the risk of poor prognosis, assuming that serum STC1, as a prognostic parameter, may be clinically valuable in ICH.

Antibiotic tolerance and degradation capacity of the organic pollutant-degrading bacterium Rhodococcus biphenylivorans TG9T.

Antibiotics are ubiquitous in soil due to natural ecological competition, as well as emerging contaminants due to anthropogenic inputs. Under environmental factors like antibiotic stress, some bacteria, including those that degrade environmental pollutants, can enter a dormant state as a survival strategy, thereby limiting their metabolic activity and function. Dormancy has a critical influence on the degradative activity of bacteria, dramatically decreasing the rate at which they transform organic pollutants. To better understand this phenomenon in environmental pollutant-degrading bacteria, we investigated dormancy transitions induced with norfloxacin in Rhodococcus biphenylivorans TG9T using next-generation proteomics, proteogenomics, and additional experiments. Our results suggest that exposure to norfloxacin inhibited DNA replication, which led to damage to the cell. Dormant cells then likely triggered DNA repair, particularly homologous recombination, for continued survival. The results also indicated that substrate transport (ATP-binding cassette transporter), ATP production, and the tricarboxylic acid (TCA) cycle were repressed during dormancy, and degradation of organic pollutants was down-regulated. Given the widespread phenomenon of dormancy among bacteria involved in pollutant removal systems, this study improves our understanding of possible implications of antibiotic survival strategies on biotransformation of mixtures containing antibiotics as well as other organics.

Application and Development of Intelligent Medicine in Traditional Chinese Medicine.

As modern science and technology constantly progresses, the fields of artificial intelligence, mixed reality technology, remote technology, etc. have rapidly developed. Meanwhile, these technologies have been gradually applied to the medical field, leading to the development of intelligent medicine. What's more, intelligent medicine has greatly promoted the development of traditional Chinese medicine (TCM), causing huge changes in the diagnosis of TCM ailments, remote treatment, teaching, etc. Therefore, there are both opportunities and challenges for inheriting and developing TCM. Herein, the related research progress of intelligent medicine in the TCM in China and abroad over the years is analyzed, with the purpose of introducing the present application status of intelligent medicine in TCM and providing reference for the inheritance and development of TCM in a new era.

Application of Artificial Intelligence in Medicine: An Overview.

Artificial intelligence (AI) is a new technical discipline that uses computer technology to research and develop the theory, method, technique, and application system for the simulation, extension, and expansion of human intelligence. With the assistance of new AI technology, the traditional medical environment has changed a lot. For example, a patient's diagnosis based on radiological, pathological, endoscopic, ultrasonographic, and biochemical examinations has been effectively promoted with a higher accuracy and a lower human workload. The medical treatments during the perioperative period, including the preoperative preparation, surgical period, and postoperative recovery period, have been significantly enhanced with better surgical effects. In addition, AI technology has also played a crucial role in medical drug production, medical management, and medical education, taking them into a new direction. The purpose of this review is to introduce the application of AI in medicine and to provide an outlook of future trends.

Anaerobic condition induces a viable but nonculturable state of the PCB-degrading Bacteria Rhodococcus biphenylivorans TG9.

Significant microbial removal of highly chlorinated polychlorinated biphenyls (PCBs) requires the cooperation of anaerobic and aerobic bacteria. During the sequencing process of anaerobic dechlorination and aerobic degradation of PCBs, aerobic degrading bacteria have to undergo anaerobic stress. However, the survival strategy of aerobic degrading bacteria under anaerobic condition is not well-understood. In this study, the culturable cells of Rhodococcus biphenylivorans TG9 decreased from 108 CFU/mL to values below the detection limit after 60 days of anaerobic stress while the viable cells remained 105-106 cells/mL, indicating that anaerobic condition induced TG9 entering into the viable but nonculturable (VBNC) state. Cell resuscitation was observed when oxygen was supplied further confirming the VBNC state of TG9. The results of single-cell Raman spectroscopy combined with heavy water indicated the significant decrease of metabolic activity after TG9 entering into the VBNC state. Additionally, the degradation ability of TG9 in the VBNC state was also significantly reduced, while it recovered after resuscitation. Our research proved that entering into the VBNC state is a survival strategy of TG9 under anaerobic conditions, and the limited culturability and degrading capacity could be overcome by resuscitation. The present study provides new insights for improving the remediation efficiency of PCBs contamination.

Integration of Artificial Intelligence, Blockchain, and Wearable Technology for Chronic Disease Management: A New Paradigm in Smart Healthcare.

Chronic diseases are a growing concern worldwide, with nearly 25% of adults suffering from one or more chronic health conditions, thus placing a heavy burden on individuals, families, and healthcare systems. With the advent of the "Smart Healthcare" era, a series of cutting-edge technologies has brought new experiences to the management of chronic diseases. Among them, smart wearable technology not only helps people pursue a healthier lifestyle but also provides a continuous flow of healthcare data for disease diagnosis and treatment by actively recording physiological parameters and tracking the metabolic state. However, how to organize and analyze the data to achieve the ultimate goal of improving chronic disease management, in terms of quality of life, patient outcomes, and privacy protection, is an urgent issue that needs to be addressed. Artificial intelligence (AI) can provide intelligent suggestions by analyzing a patient's physiological data from wearable devices for the diagnosis and treatment of diseases. In addition, blockchain can improve healthcare services by authorizing decentralized data sharing, protecting the privacy of users, providing data empowerment, and ensuring the reliability of data management. Integrating AI, blockchain, and wearable technology could optimize the existing chronic disease management models, with a shift from a hospital-centered model to a patient-centered one. In this paper, we conceptually demonstrate a patient-centric technical framework based on AI, blockchain, and wearable technology and further explore the application of these integrated technologies in chronic disease management. Finally, the shortcomings of this new paradigm and future research directions are also discussed.

Artificial Intelligence to Diagnose Tibial Plateau Fractures: An Intelligent Assistant for Orthopedic Physicians.

OBJECTIVE: To explore a new artificial intelligence (AI)-aided method to assist the clinical diagnosis of tibial plateau fractures (TPFs) and further measure its validity and feasibility. METHODS: A total of 542 X-rays of TPFs were collected as a reference database. An AI algorithm (RetinaNet) was trained to analyze and detect TPF on the X-rays. The ability of the AI algorithm was determined by indexes such as detection accuracy and time taken for analysis. The algorithm performance was also compared with orthopedic physicians. RESULTS: The AI algorithm showed a detection accuracy of 0.91 for the identification of TPF, which was similar to the performance of orthopedic physicians (0.92±0.03). The average time spent for analysis of the AI was 0.56 s, which was 16 times faster than human performance (8.44±3.26 s). CONCLUSION: The AI algorithm is a valid and efficient method for the clinical diagnosis of TPF. It can be a useful assistant for orthopedic physicians, which largely promotes clinical workflow and further guarantees the health and security of patients.

Application of AI and IoT in Clinical Medicine: Summary and Challenges.

The application of artificial intelligence (AI) technology in the medical field has experienced a long history of development. In turn, some long-standing points and challenges in the medical field have also prompted diverse research teams to continue to explore AI in depth. With the development of advanced technologies such as the Internet of Things (IoT), cloud computing, big data, and 5G mobile networks, AI technology has been more widely adopted in the medical field. In addition, the in-depth integration of AI and IoT technology enables the gradual improvement of medical diagnosis and treatment capabilities so as to provide services to the public in a more effective way. In this work, we examine the technical basis of IoT, cloud computing, big data analysis and machine learning involved in clinical medicine, combined with concepts of specific algorithms such as activity recognition, behavior recognition, anomaly detection, assistant decision-making system, to describe the scenario-based applications of remote diagnosis and treatment collaboration, neonatal intensive care unit, cardiology intensive care unit, emergency first aid, venous thromboembolism, monitoring nursing, image-assisted diagnosis, etc. We also systematically summarize the application of AI and IoT in clinical medicine, analyze the main challenges thereof, and comment on the trends and future developments in this field.

An Optimized Method to Assess Viable Escherichia coli O157:H7 in Agricultural Soil Using Combined Propidium Monoazide Staining and Quantitative PCR.

Agricultural soil contaminated by manure is becoming an important source for the transmission of foodborne pathogens. There is an urgent need for a rapid and accurate method for viable pathogen detection in agricultural soil samples. Propidium monoazide (PMA) is a DNA-binding dye that can inhibit the amplification of DNA from dead cells through subsequent quantitative polymerase chain reaction (qPCR), thus allowing for viable cells detection and quantification. The objective of this study was to detect viable Escherichia coli O157:H7 in the agricultural soils by PMA-qPCR. In this study, cell extraction and gradient density centrifugation were incorporated before PMA-qPCR to reduce the interference of soil particle including turbidity and a high ratio of dead cells. The optimized treatment conditions were determined as follows, the maximum removal of DNA from dead cells was achieved by 1.067 g/mL Percoll of centrifugation and 50 µM PMA treatment. Under these conditions, the turbidity of paddy soil suspensions decreased from 3500 to 28.4 nephelometric turbidity units (NTU), and the ratio of viable cells to dead cells increased from 0.001 to 1.025%. For typical agricultural soils collected in China, as low as 102colony-forming units (CFU)/g of viable cells could be accurately detected in the presence of a large number of dead cells (107 CFU/g) by the optimized PMA-qPCR. Significantly, with comparable accuracy, the optimized PMA-qPCR assay was more sensitive, accessible and rapid than conventional culture methods. In addition, the viable but non-culturable (VBNC) state of E. coli O157:H7 cells in paddy soils, which often escaped the detection by conventional culture methods, could be quantitatively characterized by the optimized PMA-qPCR method. Potentially, the optimized PMA-qPCR can be further applied for viable pathogens detection and give insight into the prevalence of VBNC E. coli O157:H7 in agricultural soil.

Cr(VI) removal by Penicillium oxalicum SL2: Reduction with acidic metabolites and form transformation in the mycelium.

Bioremediation of Cr(VI) contamination using microorganisms is a promising method for reducing its environmental risks. The objective of this study was to clarify Cr(VI) removal by Penicillium oxalicum SL2 in terms of indirect Cr(VI) reduction by metabolites, interaction sites, and form transformation of chromium. Strain SL2 could sequentially remove Cr(VI) in the bioreactor. Oxalic acid produced by the fungus contributed to Cr(VI) reduction. Scanning transmissiony X-ray microscop (STXM) analysis suggested strain SL2 could partly reduce Cr(VI) to Cr(III) in the cell. Amine, carboxyl, and phosphate groups were related to Cr(VI) removal. Chromium K-edge X-ray absorption near edge structure (XANES) analysis implied Cr(III)-Cys potentially acted as an intermediate for the formation of chromium oxalate complexes during the process of treatment. This study would support the application of strain SL2 in Cr(VI) bioremediation and expand knowledge on the interaction of chromium with fungus.