Monitoring Of Dynamic Changes Research Articles

Lithium-ion point-of-care ultrasound battery joint state of charge estimation

With recent advances in ultrasound technology, including accessibility and portability, point-of-care ultrasound systems (POCUS) are becoming ubiquitous in urban and rural health services. The efficient use of POCUS battery power is necessary due to the limited access to electricity in rural locations. Accurate estimation of the state-of-charge (SOC) of the onboard lithium-ion battery is crucial in ensuring the safe, efficient, and reliable operation of the battery management system (BMS). This research suggests a novel approach for striking a reasonable balance between the POCUS battery management systems computation time and SOC estimate accuracy. Given that the lithium-ion battery is a non-linear and time-varying system, it is modelled by a second-order RC equivalent circuit model (2-RC ECM). Real-time parametric identification is carried out using the method of recursive least squares with respect to an adaptive forgetting factor (AFFRLS). The joint SOC evaluation technique, ordering the results of real-time identification and the Extended Kalman Filter (EKF) recommended for estimating battery SOC at temperatures between 0 and 45 °C. Simulations were carried out using experimental data from the DST (Dynamic Stress Test), incremental current and, the Federal Urban Driving Program (FUDS) of the CALCE research group to evaluate the proposed approach. Based on the monitoring of dynamic changes in model parameters and the computational complexity of existing algorithms, the results reveal that the joint AFFRLS-EKF estimation proposed for the SOC strategy achieves good accuracy, high adaptability and rapid convergence of SOC estimation.

Single-Molecule Electrical Profiling of Peptides and Proteins.

In recent decades, there has been a significant increase in the application of single-molecule electrical analysis platforms in studying proteins and peptides. These advanced analysis methods have the potential for deep investigation of enzymatic working mechanisms and accurate monitoring of dynamic changes in protein configurations, which are often challenging to achieve in ensemble measurements. In this work, the prominent research progress in peptide and protein-related studies are surveyed using electronic devices with single-molecule/single-event sensitivity, including single-molecule junctions, single-molecule field-effect transistors, and nanopores. In particular, the successful commercial application of nanopores in DNA sequencing has made it one of the most promising techniques in protein sequencing at the single-molecule level. From single peptides to protein complexes, the correlation between their electrical characteristics, structures, and biological functions is gradually being established. This enables to distinguish different molecular configurations of these biomacromolecules through real-time electrical monitoring of their life activities, significantly improving the understanding of the mechanisms underlying various life processes.

Peculiarities of personalized selection of antipsychotic drugs for schizophrenia treatment

Determining the optimal antipsychotic drugs, its effective dose, duration of therapy, form, and route of administration play a key role in the treatment of schizophrenia. In addition, special attention should be paid to the effectiveness of using different forms of antipsychotic drugs, in particular, the orally disintegrating form as exemplified by olanzapine. To study the peculiarities of a personalized approach in the use of antipsychotic drugs to achieve more effective results in treating schizophrenia, a content analysis was conducted using Ukrainian and English-language publications for the past 15 years. The search was conducted using the PubMed and CrossRef databases. An important conclusion is that the optimal therapeutic formula or drug should be selected individually, considering the specific patient's clinical condition. However, the most important factor in achieving successful results is the individually selected form and dose of the antipsychotic drugs. In addition to the patient's mental state, the choice of antipsychotic therapy is influenced by the spectrum of side effects, individual sensitivity to the active substance, pharmacological history, economic factors, etc. This is especially true for patients with insufficient adherence to treatment, which can often arise due to the side effects of drugs. In this case, it is important to correctly select both the active substance and the appropriate route of administration. Personalized selection of antipsychotic drugs also involves dynamic monitoring of changes in the patient's clinical condition, allowing for timely diagnosis of drug side effects, dose adjustments, or changes in the route of administration. These measures help increase patient adherence to treatment and improve their health-related quality of life.

Pursuing dynamics of minimal residual leukemic subclones in relapsed and refractory acute myeloid leukemia during conventional therapy.

Acute myeloid leukemia (AML) is characterized by clonal heterogeneity, leading to frequent relapses and drug resistance despite intensive clinical therapy. Although AML's clonal architecture has been addressed in many studies, practical monitoring of dynamic changes in those subclones during relapse and treatment is still understudied. Fifteen longitudinal bone marrow (BM) samples were collected from three relapsed and refractory (R/R) AML patients. Using droplet digital polymerase chain reaction (ddPCR), the frequencies of patient's leukemic variants were assessed in seven cell populations that were isolated from each BM sample based on cellular phenotypes. By quantifying mutant clones at the diagnosis, remission, and relapse stages, the distribution of AML subclones was sequentially monitored. Minimal residual (MR) leukemic subclones exhibit heterogeneous distribution among BM cell populations, including mature leukocyte populations. During AML progression, these subclones undergo active phenotypic transitions and repopulate into distinct cell population regardless of normal hematopoiesis hierarchic order. Of these, MR subclones in progenitor populations of patient BM predominantly carry MR leukemic properties, leading to more robust expansion and stubborn persistence than those in mature populations. Moreover, a minor subset of MR leukemic subclones could be sustained at an extremely low frequency without clonal expansion during relapse. In this study, we observed treatment persistent MR leukemic subclones and their phenotypic changes during the treatment process of R/R AML patients. This underscores the importance of preemptive inhibition of clonal promiscuity in R/R AML, proposing a practical method for monitoring AML MR subclones.

A Multimodal Bio‐Sensing and Imaging Platform (MSIP) for Real‐Time Intraoperative Margin Detection in Cancer Surgery

AbstractIn cancer surgery, the precise characterisation of tissue margins is of paramount importance to ensure effective resection. However, conventional methods have inherent limitations in terms of both speed and accuracy, often necessitating labour‐intensive postsurgical tissue analyses. To overcome these challenges and enhance the precision of cancer surgery, we introduced an innovative Multimodal Sensing and Imaging Platform (MSIP) equipped with advanced pH sensing capabilities. The MSIP represents the synergistic fusion of a miniaturised, flexible wire‐based electrochemical pH sensor with a sensitivity of 62.4 mV per pH for real‐time functional imaging. Simultaneously, it incorporates a high‐speed confocal endomicroscope utilizing a 2.6 mm fibre bundle to achieve subcellular resolution in morphological imaging. The pH sensor enables continuous monitoring of dynamic changes in the tumour microenvironment, offering valuable insights. Confocal endomicroscopy provides high‐resolution, real‐time imaging of tumour morphology with an impressive 2.2 μm resolution. This integration not only enhances the precision of cancer surgery but also fosters a comprehensive understanding of the biological and molecular characteristics of tumours. By consolidating multiple diagnostic parameters into a unified platform, MSIP has emerged as a transformative technology poised to revolutionise our approach to cancer surgery.

Unraveling interactions and priorities under sustainable development goals in less-developed mountainous areas: case study on the National Innovation Demonstration Zone for the 2030 Agenda for Sustainable Development, China.

Understanding the intricate relationships between progress and the United Nations' 17 Sustainable Development Goals (SDGs) is vital for informed and adaptable sustainable development policy formulation. This study focused on the Lincang National Innovation Demonstration Zone for the 2030 Agenda for Sustainable Development (LC-NIDZASD) in China. By evaluating sustainability scores at the county level from 2011 to 2020, the trade-offs and synergies among SDGs were explored. Priority SDGs for development were identified, and targeted recommendations were established based on these findings. The key findings are as follows: (1) The SDG index scores of Lincang and its counties showed an increase from 2011 to 2020, with scores riding from 42.1 to 52.2. SDG6 (Clean Water and Sanitation) and SDG12 (Responsible Production and Consumption) had the highest scores, while SDG1 (No Poverty) and SDG4 (Quality Education) increased significantly. However, the COVID-19 pandemic led to a decrease in the scores of SDG1, SDG8 (Decent Jobs and Economic Growth), and SDG17 (Partnerships for the Goals) in 2020 decreased compared to 2019. Decreased scores in SDG13 (Climate Action) and SDG15 (Life on Land) may be attributable to climate change. (2) The relationship between "Objectives" and ''Governance" appears to be synergistic, while ''Essential Needs" mainly shows a trade-off relationship with ''Objectives" and ''Governance." (3) To promote achievements in the construction of LC-NIDZASD, priority should be given to SDG3 (Health and Well-Being), SDG8, SDG9 (Industry, Innovation, and Infrastructure), and SDG12; SDG4 should not be ignored. (4) Overall, Lincang has made significant progress in sustainable development. However, to further consolidate these achievements, adjustments should be made for SDG7 (Energy Consumption and Production Structure). Efforts should be made to strengthen climate governance measures and improve warning and forecasting capabilities to promote the synergistic development of SDG7 (Affordable and Clean Energy) and SDG13 with other SDGs. This study's dynamic monitoring of changes in the SDGs in Lincang provides valuable insights into the synergies and trade-offs among these goals. Appropriate prioritization across various SDGs can allow for timely adjustments in sustainable management policies, ultimately contributing to the successful operation of the LC-NIDZASD.

Extraction the Spatial Distribution of Mangroves in the Same Month Based on Images Reconstructed with the FSDAF Model

Mangroves have extremely high economic and ecological value. Through remote sensing, the spatial distribution of and spatiotemporal changes in mangroves can be accurately obtained, providing data support for the sustainable development of coastal wetlands. However, due to the cloudy and rainy conditions in the growing areas of mangroves, there are relatively few high-quality image data available, resulting in a time difference between regional mosaic images, with a maximum difference of several months, which has a certain impact on accuracy when extracting the spatial distribution of mangroves in some regions. At present, most regional mangrove research has ignored the impact of the time difference between mosaic images, which not only leads to inaccurate monitoring results of mangroves’ spatial distribution and dynamic changes but also limits the frequency of monitoring of regional mangrove dynamic changes to an annual scale, making it difficult to achieve more refined time scales. Based on this, this study takes the coastal mangrove distribution area in China as the research area, uses Landsat 8 and MODIS images as basic data, reconstructs the January 2021 images of the research area based on the FSDAF model, and uses a random forest algorithm to extract the spatial distribution of mangrove forests and analyze the landscape pattern. The results showed that the fused image based on the FSDAF model was highly similar to the validation image, with an R value of 0.85, showing a significant positive correlation, indicating that the fused image could replace the original image for mangrove extraction in the same month. The overall accuracy of the spatial distribution extraction of mangroves based on the fused image was 89.97%. The high sample separation and spectral curve changes highly similar to the validation image indicate that the fused image can more accurately obtain the spatial distribution of mangroves. Compared to the original image, the fused image based on the FSDAF model is closer to the validation image, and the fused image can reflect the changes in mangroves in time series, thus achieving accurate acquisition of dynamic change information in a short time span. It provides data and methodological support for future monitoring of dynamic changes in large-scale mangroves. The total area of mangroves in China in January 2021 based on the fused image was 27,122.4 ha, of which Guangdong had the largest mangrove area, with 12,098.34 ha, while Macao had the smallest mangrove area of only 16.74 ha. At the same time, the mangroves in Guangdong and Guangxi had a high degree of fragmentation and were severely disturbed, requiring strengthened protection efforts, while the mangroves in Hong Kong, Zhejiang, and Macao had regular shapes, benefiting from local active artificial restoration.

Naphthyridine-based neutral fluorescent probes for dynamic monitoring lipid droplet polarity

In this paper, several novel nitrogen-containing heterocycle dyes 1a-c with large conjugated planar structures based on naphthyridine derivatives were designed and synthesized, which had excellent optical properties and biological applications. Optical test showed the maximum absorption wavelengths of 1a-c were in the range of 443–485 nm, while the maximum emission wavelengths located at 523–610 nm in difference solvents. Furthermore, they exhibited polarity related fluorescence intensity changes with high fluorescence quantum yields, even up to 88% (1a in THF). Moreover, the transient absorption spectra revealed the excited-state lifetime of dye 1a became shorter with the increase of solvent polarity. Density functional theory calculations are also used to help explain the effect of the polarity of solvents on their fluorescence properties. Interestingly, the dyes 1a-c were somewhat toxic to cancer cells, while had an obvious growth-promoting effect on normal cells. Additionally, 1a-c with excellent lipid droplet (LD) targeting could be used for long-term observation of targeted cellular lis) hysiological activity and further applied to dynamic monitoring of changes in LDs polarity stimulated by TLR4 activator (LPS) in living cells. Overall, the excellent optical properties and biocompatibility of 1a-c indicate that they can serve as fluorescent probes for biological applications, and the results have important implications for understanding the physiological functions of LDs, especially in terms of morphological and polar changes.

Estimation of turnout irregularities using vehicle responses with improved BiLSTM and Gaussian process regression

Railway turnout irregularities pose a safety risk, but existing detection methods using expensive trains with inertial navigation systems and laser measurement devices are cost-prohibitive and time-consuming for high-speed railways. This paper proposes a low-cost and real-time solution for estimating railway turnout irregularities using vehicle body acceleration, allowing for continuous monitoring of dynamic changes. A Bayesian-optimized improved bidirectional long short-term memory neural network model (BO-BiLSTM) was proposed in this paper, utilizing the vehicle's vertical and lateral acceleration as input to achieve point estimation of irregularities, with an estimation error approximately 50% lower than that of traditional recurrent neural networks. In addition, an interval estimation model combining BO-BiLSTM and Gaussian process regression (BO-BiLSTM-GPR) was proposed. Compared to the traditional interval estimation method Bootstrap, under the condition of narrow interval width, GPR ensures that over 90% of the measured values fall within the estimated interval and provides a better solution to the trade-off between interval estimation reliability and uncertainty.

PLGA implants for controlled dexamethasone delivery: Impact of the polymer chemistry

The aim of this study was to better understand the impact of the chemistry of poly(lactic-co-glycolic acid) (PLGA) polymers on the resulting drug release kinetics from implants prepared by melting and molding. Dexamethasone was incorporated as the drug. The polymer molecular weight of the PLGA, lactic acid:glycolic acid ratio and type of end groups (ester versus free acid) were varied. The implants were characterized using optical macroscopy, SEM, X-ray powder diffraction, DSC, gravimetric monitoring of dynamic changes in the systems’ dry and wet mass upon exposure to the release medium as well as drug release and pH measurements. Interestingly, the shape of the drug release profiles was similar in all cases: No noteworthy burst effect was observed. Dexamethasone release set on after a lag time, the length of which strongly depended on the PLGA chemistry: The lag time decreased with decreasing polymer molecular weight, increasing glycolic acid content and was shorter for –COOH end groups compared to ester end groups. In all cases, drug release set on as soon as a critical hydrophilicity and polymer molecular weight were reached and substantial system swelling started: The penetration of large amounts of water into the implants allowed for complete dexamethasone dissolution and relatively rapid diffusion of the dissolved drug molecules through a highly swollen PLGA gel up to 100% drug release.

Study on Individual Tree Segmentation of Different Tree Species Using Different Segmentation Algorithms Based on 3D UAV Data

Individual structural parameters of trees, such as forest stand tree height and biomass, serve as the foundation for monitoring of dynamic changes in forest resources. Individual tree structural parameters are closely related to individual tree crown segmentation. Although three-dimensional (3D) data have been successfully used to determine individual tree crown segmentation, this phenomenon is influenced by various factors, such as the (i) source of 3D data, (ii) the segmentation algorithm, and (iii) the tree species. To further quantify the effect of various factors on individual tree crown segmentation, light detection and ranging (LiDAR) data and image-derived points were obtained by unmanned aerial vehicles (UAVs). Three different segmentation algorithms (PointNet++, Li2012, and layer-stacking segmentation (LSS)) were used to segment individual tree crowns for four different tree species. The results show that for two 3D data, the crown segmentation accuracy of LiDAR data was generally better than that obtained using image-derived 3D data, with a maximum difference of 0.13 in F values. For the three segmentation algorithms, the individual tree crown segmentation accuracy of the PointNet++ algorithm was the best, with an F value of 0.91, whereas the result of the LSS algorithm yields the worst result, with an F value of 0.86. Among the four tested tree species, the individual tree crown segmentation of Liriodendron chinense was the best, followed by Magnolia grandiflora and Osmanthus fragrans, whereas the individual tree crown segmentation of Ficus microcarpa was the worst. Similar crown segmentation of individual Liriodendron chinense and Magnolia grandiflora trees was observed based on LiDAR data and image-derived 3D data. The crown segmentation of individual Osmanthus fragrans and Ficus microcarpa trees was superior according to LiDAR data to that determined according to image-derived 3D data. These results demonstrate that the source of 3D data, the segmentation algorithm, and the tree species all have an impact on the crown segmentation of individual trees. The effect of the tree species is the greatest, followed by the segmentation algorithm, and the effect of the 3D data source. Consequently, in future research on individual tree crown segmentation, 3D data acquisition methods should be selected based on the tree species, and deep learning segmentation algorithms should be adopted to improve the crown segmentation of individual trees.

Visual inertial odometry enabled 3D ultrasound and photoacoustic imaging.

There is an increasing need for 3D ultrasound and photoacoustic (USPA) imaging technology for real-time monitoring of dynamic changes in vasculature or molecular markers in various malignancies. Current 3D USPA systems utilize expensive 3D transducer arrays, mechanical arms or limited-range linear stages to reconstruct the 3D volume of the object being imaged. In this study, we developed, characterized, and demonstrated an economical, portable, and clinically translatable handheld device for 3D USPA imaging. An off-the-shelf, low-cost visual odometry system (the Intel RealSense T265 camera equipped with simultaneous localization and mapping technology) to track free hand movements during imaging was attached to the USPA transducer. Specifically, we integrated the T265 camera into a commercially available USPA imaging probe to acquire 3D images and compared it to the reconstructed 3D volume acquired using a linear stage (ground truth). We were able to reliably detect 500 µm step sizes with 90.46% accuracy. Various users evaluated the potential of handheld scanning, and the volume calculated from the motion-compensated image was not significantly different from the ground truth. Overall, our results, for the first time, established the use of an off-the-shelf and low-cost visual odometry system for freehand 3D USPA imaging that can be seamlessly integrated into several photoacoustic imaging systems for various clinical applications.

Preventing rheumatoid arthritis: Lessons from that of type 1 diabetes

Preventing rheumatoid arthritis: Lessons from that of type 1 diabetes

Surveillance of the progression and assessment of treatment endpoints for nonalcoholic steatohepatitis.

Nonalcoholic steatohepatitis (NASH) is an aggressive form of nonalcoholic fatty liver disease (NAFLD) characterized by steatosis-associated inflammation and liver injury. Without effective treatment or management, NASH can have life-threatening outcomes. Evaluation and identification of NASH patients at risk for adverse outcomes are therefore important. Key issues in screening NASH patients are the assessment of advanced fibrosis, differentiation of NASH from simple steatosis, and monitoring of dynamic changes during follow-up and treatment. Currently, NASH staging and evaluation of the effectiveness for drugs still rely on pathological diagnosis, despite sample error issues and the subjectivity associated with liver biopsy. Optimizing the pathological assessment of liver biopsy samples and developing noninvasive surrogate methods for accessible, accurate, and safe evaluation are therefore critical. Although noninvasive methods including elastography, serum soluble biomarkers, and combined models have been implemented in the last decade, noninvasive diagnostic measurements are not widely applied in clinical practice. More work remains to be done in establishing cost-effective strategies both for screening for at-risk NASH patients and identifying changes in disease severity. In this review, we summarize the current state of noninvasive methods for detecting steatosis, steatohepatitis, and fibrosis in patients with NASH, and discuss noninvasive assessments for screening at-risk patients with a focus on the characteristics that should be monitored at follow-up.

Dynamic Monitoring of Changes in Fecal Flora of Giant Pandas in Mice: Co-Occurrence Network Reconstruction.

Giant pandas are uniquely vulnerable mammals in western China. It is important to develop an animal model to explore the intestinal flora of giant pandas to understand the relationship between digestive diseases and flora. Existing animal models of intestinal flora focus on human flora-associated animals, such as mice, and there is a very limited amount of knowledge regarding giant panda flora-associated animals. To fill this gap, fecal microorganisms from giant pandas were transplanted into pseudosterile and germfree mice using single and multiple gavages. Fecal samples were collected from mice at four time points after transplantation for microbial community analysis. We determined that compared to pseudosterile mice, the characteristics of intestinal flora in pandas were better reproduced in germfree mice. There was no significant difference in microbial diversity between germfree mice and giant panda gut microbes from day 3 to day 21. Germfree mice at the phylum level possessed large amounts of Firmicutes and Proteobacteria, and at the genus level, Escherichia-Shigella, Clostridium sensu stricto 1, and Streptococcus dominated the intestinal flora structure. The microbial community co-occurrence network based on indicator species indicated that germfree mice transplanted with fecal bacteria tended to form a microbial community co-occurrence network similar to that of giant pandas, while pseudosterile mice tended to restore the microbial community co-occurrence network originally present in these mice. Our data are helpful for the study of giant panda flora-associated animals and provide new insights for the in vitro study of giant panda intestinal flora. IMPORTANCE The giant panda is a unique vulnerable mammal in western China, and its main cause of death is digestive system diseases regardless of whether these animals are in the wild or in captivity. The relationship between the intestinal flora and the host exerts a significant impact on the nutrition and health of the giant pandas. However, the protected status of the giant panda has made in vivo, repeatable, and large-sample sampling studies of their intestinal flora difficult. This greatly hinders the research depth of the giant panda intestinal flora from the source. The development and utilization of specific animal models to simulate the structure and characteristics of the intestinal flora provide another means to deal with these research limitations. However, current research examining giant panda flora-associated animals is limited. This study is the first to reveal dynamic changes in the fecal flora of giant pandas in mice after transplantation.

A novel peptide-based relay fluorescent probe with a large Stokes shift for detection of Hg2+ and S2− in 100 % aqueous medium and living cells: Visual detection via test strips and smartphone

Herein, a novel relay peptide-based fluorescent probe DGRK was synthesized via solid phase peptide synthesis (SPPS) technology. DGRK exhibited excellent water-solubility, good stability, remarkably large Stokes shift (230 nm) and high selectivity response to Hg2+ with a non-fluorescence complex DGRK-Hg2+ formation via a 1:1 binding mode. Further studies indicated that the DGRK-Hg2+ complex could act as a secondary probe for rapidly and sequentially detecting S2− based on fluorescent “off–on” response, and without interference from a range of anions. The limit of detection (LOD) for Hg2+ and S2− were calculated to be 33.6 nM and 60.9 nM, respectively. In addition, The reversibility of interaction of confirmed that the continuous and reversible recognition behavior of Hg2+ and S2− by the probe DGRK, and could be cycled more than 5 times. In addition, DGRK could be successfully applied to the fluorescence imaging of Hg2+ and S2− in two living cells based on excellent cells permeability and low cytotoxicity. Meanwhile, DGRK was successfully used to create the low-cost and portable test strips for visual detection and rapid analysis under 365 nm UV lamp, and the test strips combined with a smartphone (RGB color) was successfully applied to the semi-quantitative analysis and monitoring of dynamic changes of Hg2+ levels.

Near-infrared probe for early diagnosis of diabetic complications-nephropathy and in vivo visualization fluorescence imaging research

Diabetic nephropathy is one of the common complications of diabetes, which has high risk of renal function. Dipeptidyl peptidase 4 (DPP4) is considered to be one of the good dynamic monitoring indicators for early diabetic nephropathy. Therefore, real-time monitoring of changes in the activity of DPP4 in organisms is helpful to the diagnosis and treatment of diabetes and its complications-diabetic nephropathy. A near-infrared fluorescent probe GP-DCMNH2 is designed to detect the activity of DPP4. GP-DCMNH2 is catalyzed and hydrolyzed by DPP4 into the near-infrared fluorescent dye DCMNH2, to achieve the purpose of detecting DPP4 in organisms. Based on the excellent near-infrared spectroscopy characteristics displayed by the probe GP-DCMNH2 in vitro, in living cells and diabetic mouse models, GP-DCMNH2 has been further applied in the visual fluorescence imaging of diabetic complications-diabetic nephropathy. Compared with the control group and the treatment group, GP-DCMNH2 showed a stronger near-infrared fluorescence signal in the kidney tissue and blood of diabetic nephropathy mice. Because GP-DCMNH2 shows high sensitivity in real-time dynamic monitoring of changes in the activity of DPP4 in organisms, and shows strong practicability in the spectral test of mouse models of diabetes and diabetic nephropathy. In clinical medicine, GP-DCMNH2 is expected to be used in the early diagnosis, prevention and treatment of diabetes and diabetic nephropathy.

Quality of Life Study in Patients with Multiple Sclerosis and Depression

Background: quality of life (QOL) considered an integral characteristic of patient’s physical, psychological, emotional and social functioning. An improvement of QOL is one of the tasks of therapeutical intervention in patients with Multiple Sclerosis (MS). The aim was to study QoL of patients with MS and depression, using different instruments of QoL assessment. Patients and Methods: the study made up 203 MS-patients (56 males and 147 females) aged from 15–60 years (mean age 39.7 ± 10.91). The diagnosis of MS established on criteria of W. McDonald (2010, 2017). There were 113 patients with MS and depression. One group of comparison included 90 MS-patients without depression. The other compared group consisted from 70 depressive patients without neurological disease. The mean age of 52 healthy persons was 29.25 ± 5.12. QOL was assessed using The Short Form-36 (SF-36) and Multiple Sclerosis Quality of Life-54 (MSQOL-54). The study presents comparative assessments of the conducted SF-36 and MSQoL-54 questionnaires, which show that their use helps to assess the quality of life of patients with health disorders in MS patients with depression. Each of methods supplements information about the condition of patients, depending on the purpose and nosology. Conclusion: both questionnaires could be used for dynamic monitoring of changes in the quality of life in patients with MS with depression, as well as to determine the impact of the disease on a person and to seek for adequate ways of social and psychiatric care and rehabilitation of patients.

Exploring the role of Microbiome in Susceptibility, Treatment Response and Outcome among Tuberculosis Patients from Pakistan: study protocol for a prospective cohort study (Micro-STOP)

IntroductionTuberculosis (TB) caused by Mycobacterium tuberculosis is a common infectious disease associated with significant morbidity and mortality, especially in low-income and middle-income countries. Successful treatment of the disease requires prolonged...

LONG-TERM LAND SURFACE WATER MONITORING IN THE YELLOW RIVER BASIN OF CHINA BASED ON LANDSAT IMAGERY ON THE GOOGLE EARTH ENGINE

Abstract. Land surface water is the most active part of the earth's surface layer and plays an important role in material balance and energy transformation. The monitoring of dynamic changes in surface open water helps to grasp the distribution and changes of water resources, which is important for understanding the impact of human activities on water resources and safeguarding the ecological security of the basin. In the study, 72,520 Landsat images were processed using the Google Earth Engine (GEE) cloud platform to study the long-term changes of the open surface water in the Yellow River Basin (YRB) from 2000 to 2020. The results show that (1) The multi-index water body extraction rules (MIWER) is suitable for long-term and large-scale surface water mapping based on Landsat images; (2) From 2000 to 2020, both permanent and seasonal water in the whole YRB and sub-basins have increased. In general, the total open surface water in the YRB show an increasing trend with obvious spatial heterogeneity; (3) The changes of open surface water in the YRB are related to climate change and intensive human activities, but the influencing factors vary in different regions and need to be further studied in the future. The findings of this study can be used to help the policy-makers understand changing water resources and their driving mechanism and provide a reference for water resources management, agricultural irrigation, and ecological protection.