Daily Posture Behavior Patterns Derived From Multitime-Scale Topic Models Using Wearable Triaxial Acceleration for Assessment of Concern About Falling.

Concern about falling is prevalent in older population. This condition would cause a series of adverse physical and psychological consequences for older adults' health. Traditional assessment of concern about falling is relied on self-reported questionnaires and thus is too subjective. Therefore, we proposed a novel multi-time-scale topic modelling approach to quantitatively evaluate concern about falling by analyzing triaxial acceleration signals collected from a wearable pendent sensor. Different posture segments were firstly recognized to extract their corresponding feature subsets. Then, each selected feature related to concern about falling was clustered into discrete levels as feature letters of artificial words in different time scales. As a result, all older participants' signal recordings were converted to a collection of artificial documents, which can be processed by natural language processing methodologies. The topic modelling technique was used to discover daily posture behavior patterns from these documents as discriminants between older adults with different levels of concern about falling. The results indicated that there were significant differences in distributions of posture topics between groups of older adults with different levels of concern about falling. Additionally, the transitions of posture topics over daytime and nighttime revealed temporal regularities of posture behavior patterns of older adult's active and inactive status, which were substantially different for older adults with different levels of concern about falling. Finally, the level of concern about falling was accurately determined with accuracy of 71.2% based on the distributions of posture topics combined with the mobility performance metrics of walking behaviors and demographic information.

Epidemiological characteristics and dynamic transmissions of COVID-19 pandemics in Chinese mainland: A trajectory clustering perspective analysis.

BACKGROUND: The corona virus disease 2019 (COVID-19) pandemic has spread to more than 210 countries and regions around the world, with different characteristics recorded depending on the location. A systematic summarization of COVID-19 outbreaks that occurred during the "dynamic zero-COVID" policy period in Chinese mainland had not been previously conducted. In-depth mining of the big data from the past two years of the COVID-19 pandemics must be performed to clarify their epidemiological characteristics and dynamic transmissions. METHODS: Trajectory clustering was used to group epidemic and time-varying reproduction number (Rt) curves of mass outbreaks into different models and reveal the epidemiological characteristics and dynamic transmissions of COVID-19. For the selected single-peak epidemic curves, we constructed a peak-point judgment model based on the dynamic slope and adopted a single-peak fitting model to identify the key time points and peak parameters. Finally, we developed an extreme gradient boosting-based prediction model for peak infection cases based on the total number of infections on the first 3, 5, and 7 days of the initial average incubation period. RESULTS: (1) A total of 7 52298 cases, including 587 outbreaks in 251 cities in Chinese mainland between June 11, 2020, and June 29, 2022, were collected, and the first wave of COVID-19 outbreaks was excluded. Excluding the Shanghai outbreak in 2022, the 586 remaining outbreaks resulted in 1 25425 infections, with an infection rate of 4.21 per 1 00000 individuals. The number of outbreaks varied based on location, season, and temperature. (2) Trajectory clustering analysis showed that 77 epidemic curves were divided into four patterns, which were dominated by two single-peak clustering patterns (63.3%). A total of 77 Rt curves were grouped into seven patterns, with the leading patterns including four downward dynamic transmission patterns (74.03%). These curves revealed that the interval from peak to the point where the Rt value dropped below 1 was approximately 5 days. (3) The peak-point judgment model achieved a better result in the area under the curve (0.96, 95% confidence interval = 0.90-1.00). The single-peak fitting results on the epidemic curves indicated that the interval from the slow-growth point to the sharp-decline point was approximately 4-6 days in more than 50% of mass outbreaks. (4) The peak-infection-case prediction model exhibited the superior clustering results of epidemic and Rt curves compared with the findings without grouping. CONCLUSION: Overall, our findings suggest the variation in the infection rates during the "dynamic zero-COVID" policy period based on the geographic division, level of economic development, seasonal division, and temperature. Trajectory clustering can be a useful tool for discovering epidemiological characteristics and dynamic transmissions, judging peak points, and predicting peak infection cases using different patterns.

Synergistic Effects of Earthworms and Plants on Chromium Removal from Acidic and Alkaline Soils: Biological Responses and Implications.

Soil heavy metal pollution has become one of the major environmental issues of global concern and solving this problem is a major scientific and technological need for today's socio-economic development. Environmentally friendly bioremediation methods are currently the most commonly used for soil heavy metal pollution remediation. Via controlled experiments, the removal characteristics of chromium from contaminated soil were studied using earthworms (Eisenia fetida and Pheretima guillelmi) and plants (ryegrass and maize) at different chromium concentrations (15 mg/kg and 50 mg/kg) in acidic and alkaline soils. The effects of chromium contamination on biomass, chromium bioaccumulation, and earthworm gut microbial communities were also analyzed. The results showed that E. fetida had a relatively stronger ability to remove chromium from acidic and alkaline soil than P. guillelmi, and ryegrass had a significantly better ability to remove chromium from acidic and alkaline soil than maize. The combined use of E. fetida and ryegrass showed the best effect of removing chromium from contaminated soils, wih the highest removal rate (63.23%) in acidic soil at low Cr concentrations. After soil ingestion by earthworms, the content of stable chromium (residual and oxidizable forms) in the soil decreased significantly, while the content of active chromium (acid-extractable and reducible forms) increased significantly, thus promoting the enrichment of chromium in plants. The diversity in gut bacterial communities in earthworms decreased significantly following the ingestion of chromium-polluted soil, and their composition differences were significantly correlated with soil acidity and alkalinity. Bacillales, Chryseobacterium, and Citrobacter may have strong abilities to resist chromium and enhance chromium activity in acidic and alkaline soils. There was also a significant correlation between changes in enzyme activity in earthworms and their gut bacterial communities. The bacterial communities, including Pseudomonas and Verminephrobacter, were closely related to the bioavailability of chromium in soil and the degree of chromium stress in earthworms. This study provides insights into the differences in bioremediation for chromium-contaminated soils with different properties and its biological responses.

An Integrated Approach of Learning Genetic Networks From Genome-Wide Gene Expression Data Using Gaussian Graphical Model and Monte Carlo Method.

Global genetic networks provide additional information for the analysis of human diseases, beyond the traditional analysis that focuses on single genes or local networks. The Gaussian graphical model (GGM) is widely applied to learn genetic networks because it defines an undirected graph decoding the conditional dependence between genes. Many algorithms based on the GGM have been proposed for learning genetic network structures. Because the number of gene variables is typically far more than the number of samples collected, and a real genetic network is typically sparse, the graphical lasso implementation of GGM becomes a popular tool for inferring the conditional interdependence among genes. However, graphical lasso, although showing good performance in low dimensional data sets, is computationally expensive and inefficient or even unable to work directly on genome-wide gene expression data sets. In this study, the method of Monte Carlo Gaussian graphical model (MCGGM) was proposed to learn global genetic networks of genes. This method uses a Monte Carlo approach to sample subnetworks from genome-wide gene expression data and graphical lasso to learn the structures of the subnetworks. The learned subnetworks are then integrated to approximate a global genetic network. The proposed method was evaluated with a relatively small real data set of RNA-seq expression levels. The results indicate the proposed method shows a strong ability of decoding the interactions with high conditional dependences among genes. The method was then applied to genome-wide data sets of RNA-seq expression levels. The gene interactions with high interdependence from the estimated global networks show that most of the predicted gene-gene interactions have been reported in the literatures playing important roles in different human cancers. Also, the results validate the ability and reliability of the proposed method to identify high conditional dependences among genes in large-scale data sets.

Tumor-to-blood ratio for assessment of fibroblast activation protein receptor density in pancreatic cancer using [68Ga]Ga-FAPI-04.

PURPOSE: [68Ga]Ga-FAPI PET/CT has been widely used in clinical diagnosis and radiopharmaceutical therapy. In this study, tumor-to-blood ratio (TBR) was evaluated as a powerful tool for semiquantitative assessment of [68Ga]Ga-FAPI-04 tumor uptake and as an effective index for tumors with high FAP expression in theranostics. METHODS: Nine patients with pancreatic cancer underwent a 60-min dynamic PET/CT scan by total-body PET/CT (with a long AFOV of 194 cm) after injection of [68Ga]Ga-FAPI-04. After dynamic PET/CT scan, three patients received chemotherapy and underwent the second dynamic scan to evaluate treatment response. Time-activity curves (TACs) were obtained by drawing regions of interest for primary pancreatic lesions and metastatic lesions. The lesion TACs were fitted using four compartment models by the software PMOD PKIN kinetic modeling. The preferred pharmacokinetic model for [68Ga]Ga-FAPI-04 was evaluated based on the Akaike information criterion. The correlations between simplified methods for quantification of [68Ga]Ga-FAPI-04 (SUVs; tumor-to-blood ratios [TBRs]) and the total distribution volume (Vt) estimates obtained from pharmacokinetic analysis were calculated. RESULTS: In total, 9 primary lesions and 25 metastatic lesions were evaluated. The reversible two-tissue compartment model (2TCM) was the most appropriate model among the four compartment models. The total distribution volume Vt values derived from 2TCM varied significantly in pathological lesions and background regions. A strong positive correlation was observed between TBRmean and Vt from the 2TCM model in pathological lesions (R2=0.92, P<0.001). The relative difference range for TBRmean was 2.1% compared to the reduction rate of Vt in the patients who were treated with chemotherapy. CONCLUSIONS: A strong positive correlation was observed between TBRmean and Vt for [68Ga]Ga-FAPI-04. TBRmean reflects FAP receptor density better than SUVmean and SUVmax, and would be the preferred measurement tool for semiquantitative assessment of [68Ga]Ga-FAPI-04 tumor uptake and as a means for evaluating treatment response.

A real-time optimization method for economic and effective operation of electrostatic precipitators.

Electrostatic precipitators (ESP) have been considered as the main particulate matter (PM) removal facility in the energy industry. This paper presents a real-time optimization method for a one-chamber industrial ESP in an ultra-low emission power plant with an intelligent optimization system (IOS). The IOS seeks to optimize the energy consumption of ESP subject to the outlet concentration requirement in real-time. A coordination control logic is designed to regulate the optimized operation set points with varying operation conditions. The operation optimized by the IOS is compared with the operations under PID (proportion-integral-derivative) and manual control. The results show that the IOS improves the emission compliance rate from 95% of manual control to 100% and the medium concentration is tuned to be 46.6% closer to the emission target. Furthermore, a good balance between emission and energy consumption is achieved, with 35.50% energy conservation for the same emission upper limit of 30 mg/m3. These results prove that the IOS significantly contributes to the efficient operation and economic PM removal by ESP for the energy industry. IMPLICATIONS: Electrostatic precipitators (ESP) is one of the main PM removal facilities in coal-fired power plants. An intelligent optimization system (IOS) with prediction, optimization, and control modules is designed and constructed for the ESP in an ultra-low emission power plant. A PM removal model is used to predict the outlet concentration of the ESP. The optimal energy consumption of ESP subject to the outlet concentration requirement problem is solved by the particle swarm optimization. A closed-loop and rapping tolerant method is used to eliminate the fluctuation in time-averaged concentration. The system raised is able to ensure the compliance rate while decreasing the energy consumption of the ESP.

Classification of gallbladder cancer by assessment of CD8+ TIL and PD-L1 expression.

BACKGROUND: Programmed death ligand 1/2 (PD-L1/PD-L2) expression has been established as a prognostic factor for various solid tumors and as a predictive factor for PD-1 blockade therapy, but scant data on its role in gallbladder cancer (GBC). The aims of this study were to assess the expression of PD-L1/PD-L2 and the density of CD8+ tumor-infiltrating lymphocytes (TIL) from GBC samples and to quantify the association between survival prognosis and these factors. METHODS: CD8+ TILs density and the expression of PD-1, PD-L1, PD-L2 and CD133 were assessed using immunohistochemistry in tumor specimens from 66 patients with gallbladder adenocarcinoma. These indexes were correlated with the clinicopathological features. RESULTS: The rate of PD-L1-positive (PD-L1+) was 54%, which included 18% positivity in tumor cells, and 36% in peritumoral immune stroma. High CD8+ TIL density (CD8high) was observed in PD-L1+ GBC, and PD-L1+ was positively associated with PD-L2+ expression. Regarding prognostic factors, PD-L1+ expression was related to worse overall survival (OS), and CD8high indicated better OS and progression-free survival (PFS). The combination of CD8high with PD-L1+ serves as a prognostic factor for improved OS (P < 0.001) and PFS (P = 0.014). CONCLUSION: Analysis of the tumor immune microenvironment based on CD8+ TIL and PD-L1 expression is a promising independent predictor for the clinical outcome of GBC patients.

Precision medicine: In need of guidance and surveillance.

Precision medicine, currently a hotspot in mainstream medicine, has been strongly promoted in recent years. With rapid technological development, such as next-generation sequencing, and fierce competition in molecular targeted drug exploitation, precision medicine represents an advance in science and technology; it also fulfills needs in public health care. The clinical translation and application of precision medicine - especially in the prevention and treatment of tumors - is far from satisfactory; however, the aims of precision medicine deserve approval. Thus, this medical approach is currently in its infancy; it has promising prospects, but it needs to overcome numbers of problems and deficiencies. It is expected that in addition to conventional symptoms and signs, precision medicine will define disease in terms of the underlying molecular characteristics and other environmental susceptibility factors. Those expectations should be realized by constructing a novel data network, integrating clinical data from individual patients and personal genomic background with existing research on the molecular makeup of diseases. In addition, multi-omics analysis and multi-discipline collaboration will become crucial elements in precision medicine. Precision medicine deserves strong support, and its development demands directed momentum. We propose three kinds of impetus (research, application and collaboration impetus) for such directed momentum toward promoting precision medicine and accelerating its clinical translation and application.

The effects of urinary trypsin inhibitor on liver function and inflammatory factors in patients undergoing hepatectomy: a prospective, randomized, controlled clinical study.

BACKGROUND: The inhibition of inflammation exerts benefits following massive hepatectomy in animals but not in the clinic. The aim of this study was to investigate the effectiveness and mechanism of ulinastatin on liver function and outcomes following hepatectomy. METHODS: One hundred seventy-six patients undergoing hepatectomy were randomized into the treatment group (n = 86) and the control group (n = 90), receiving ulinastatin 150,000 U twice daily for 3 days and saline vehicle, respectively. Liver function, coagulation, thrombokinase, lymphocyte subsets CD4 and CD8, C-reactive protein, inducible nitric oxide synthase, and cytokines were measured. Clinical outcomes were also evaluated. RESULTS: Serum alanine transaminase, aspartate transferase, inducible nitric oxide synthase, and tumor necrosis factor-α levels were significantly lower after ulinastatin treatment, and the response of bilirubin was delayed. The benefits of ulinastatin were shown mainly in major hepatectomy earlier after surgery. The treatment significantly reduced hospital length of stay and recovery-related cost. CONCLUSIONS: Ulinastatin protects liver function and improves clinical outcomes, possibly via the inhibition of inflammation and oxidation at an earlier stage following major hepatectomy.