Effective Bidirectional Mott-Schottky Catalysts Derived from Spent LiFePO4 Cathodes for Robust Lithium-Sulfur Batteries.

It is deemed as a tough yet profound project to comprehensively cope with a range of detrimental problems of lithium-sulfur batteries (LSBs), mainly pertaining to the shuttle effect of lithium polysulfides (LiPSs) and sluggish sulfur conversion. Herein, a Co2P-Fe2P@N-doped carbon (Co2P-Fe2P@NC) Mott-Schottky catalyst is introduced to enable bidirectionally stimulated sulfur conversion. This catalyst is prepared by simple carbothermal reduction of spent LiFePO4 cathode and LiCoO2. The experimental and theoretical calculation results indicate that thanks to unique surface/interface properties derived from the Mott-Schottky effect, full anchoring of LiPSs, mediated Li2S nucleation/dissolution, and bidirectionally expedited "solid⇌liquid⇌solid" kinetics can be harvested. Consequently, the S/Co2P-Fe2P@NC manifests high reversible capacity (1569.9 mAh g-1), superb rate response (808.9 mAh g-1 at 3C), and stable cycling (a low decay rate of 0.06% within 600 cycles at 3C). Moreover, desirable capacity (5.35 mAh cm-2) and cycle stability are still available under high sulfur loadings (4-5 mg cm-2) and lean electrolyte (8 µL mg-1) conditions. Furthermore, the as-proposed universal synthetic route can be extended to the preparation of other catalysts such as Mn2P-Fe2P@NC from spent LiFePO4 and MnO2. This work unlocks the potential of carbothermal reduction phosphating to synthesize bidirectional catalysts for robust LSBs.

Biomass-related PM2.5 induced inflammatory microenvironment via IL-17F/IL-17RC axis.

Biomass exposure is a significant environmental risk factor for COPD, but the underlying mechanisms have not yet been fully elucidated. Inflammatory microenvironment has been shown to drive the development of many chronic diseases. Pollution exposure can cause increased levels of inflammatory factors in the lungs, leading to an inflammatory microenvironment which is prevalent in COPD. Our findings revealed that IL-17F was elevated in COPD, while exposure to biomass led to increased expression of IL-17F in both alveolar epithelial and macrophage cells in mice. Blocking IL-17F could alleviate the lung inflammation induced by seven days of biomass exposure in mice. We employed a transwell co-culture system to simulate the microenvironment and investigate the interactions between MLE-12 and MH-S cells. We demonstrated that anti-IL-17F antibody attenuated the inflammatory responses induced by BRPM2.5 in MLE-12 and MH-S co-cultured with BRPM2.5-MLE-12, which reduced inflammatory changes in microenvironment. We found that IL-17RC, an important receptor for IL-17F, played a key role in the interactions. Knockout of IL-17RC in MH-S resulted in inhibited IL-17F signaling and attenuated inflammatory response after MH-S co-culture with BRPM2.5-MLE-12. Our investigation suggests that BRPM2.5 induces lung epithelial-macrophage interactions via IL-17F/IL-17RC axis regulating the inflammatory response. These results may provide a novel strategy for effective prevention and treatment of biomass-related COPD.

Transformer-based multi-task learning for classification and segmentation of gastrointestinal tract endoscopic images.

Despite being widely utilized to help endoscopists identify gastrointestinal (GI) tract diseases using classification and segmentation, models based on convolutional neural network (CNN) have difficulties in distinguishing the similarities among some ambiguous types of lesions presented in endoscopic images, and in the training when lacking labeled datasets. Those will prevent CNN from further improving the accuracy of diagnosis. To address these challenges, we first proposed a Multi-task Network (TransMT-Net) capable of simultaneously learning two tasks (classification and segmentation), which has the transformer designed to learn global features and can combine the advantages of CNN in learning local features so that to achieve a more accurate prediction in identifying the lesion types and regions in GI tract endoscopic images. We further adopted the active learning in TransMT-Net to tackle the labeled image-hungry problem. A dataset was created from the CVC-ClinicDB dataset, Macau Kiang Wu Hospital, and Zhongshan Hospital to evaluate the model performance. Then, the experimental results show that our model not only achieved 96.94% accuracy in the classification task and 77.76% Dice Similarity Coefficient in the segmentation task but also outperformed those of other models on our test set. Meanwhile, active learning also produced positive results for the performance of our model with a small-scale initial training set, and even its performance with 30% of the initial training set was comparable to that of most comparable models with the full training set. Consequently, the proposed TransMT-Net has demonstrated its potential performance in GI tract endoscopic images and it through active learning can alleviate the shortage of labeled images.

CLELNet: A continual learning network for esophageal lesion analysis on endoscopic images.

BACKGROUND AND OBJECTIVE: A deep learning-based intelligent diagnosis system can significantly reduce the burden of endoscopists in the daily analysis of esophageal lesions. Considering the need to add new tasks in the diagnosis system, a deep learning model that can train a series of tasks incrementally using endoscopic images is essential for identifying the types and regions of esophageal lesions. METHOD: In this paper, we proposed a continual learning-based esophageal lesion network (CLELNet), in which a convolutional autoencoder was designed to extract representation features of endoscopic images among different esophageal lesions. The proposed CLELNet consists of shared layers and task-specific layers. Shared layers are used to extract common features among different lesions while task-specific layers can complete different tasks. The first two tasks trained by the CLELNet are the classification (task 1) and the segmentation (task 2). We collected a dataset of esophageal endoscopic images from Macau Kiang Wu Hospital for training and testing the CLELNet. RESULTS: The experimental results showed that the classification accuracy of task 1 was 95.96%, and the Intersection Over Union and the Dice Similarity Coefficient of task 2 were 65.66% and 78.08%, respectively. CONCLUSIONS: The proposed CLELNet can realize task-incremental learning without forgetting the previous tasks and thus become a useful computer-aided diagnosis system in esophageal lesions analysis.

An early warning model for predicting major adverse kidney events within 30 days in sepsis patients.

Background: In sepsis patients, kidney damage is among the most dangerous complications, with a high mortality rate. In addition, major adverse kidney events within 30 days (MAKE30) served as a comprehensive and unbiased clinical outcome measure for sepsis patients due to the recent shift toward targeting patient-centered renal outcomes in clinical research. However, the underlying predictive model for the prediction of MAKE30 in sepsis patients has not been reported in any study. Methods: A cohort of 2,849 sepsis patients from the Medical Information Mart for Intensive Care (MIMIC)-IV database was selected and subsequently allocated into a training set (n = 2,137, 75%) and a validation set (n = 712, 25%) through randomization. In addition, 142 sepsis patients from the Xi'An No. 3 Hospital as an external validation group. Univariate and multivariate logistic regression analyses were conducted to ascertain the independent predictors of MAKE30. Subsequently, a nomogram was developed utilizing these predictors, with an area under curve (AUC) above 0.6. The performance of nomogram was assessed through calibration curve, receiver operating characteristics (ROC) curve, and decision curve analysis (DCA). The secondary outcome was 30-day mortality, persistent renal dysfunction (PRD), and new renal replacement therapy (RRT). MAKE30 were a composite of death, PRD, new RRT. Results: The construction of the nomogram was based on several independent predictors (AUC above 0.6), including age, respiratory rate (RR), PaO2, lactate, and blood urea nitrogen (BUN). The predictive model demonstrated satisfactory discrimination for MAKE30, with an AUC of 0.740, 0.753, and 0.821 in the training, internal validation, and external validation cohorts, respectively. Furthermore, the simple prediction model exhibited superior predictive value compared to the SOFA model in both the training (AUC = 0.710) and validation (AUC = 0.692) cohorts. The nomogram demonstrated satisfactory calibration and clinical utility as evidenced by the calibration curve and DCA. Additionally, the predictive model exhibited excellent accuracy in forecasting 30-day mortality (AUC = 0.737), PRD (AUC = 0.639), and new RRT (AUC = 0.846) within the training dataset. Additionally, the model displayed predictive power for 30-day mortality (AUC = 0.765), PRD (AUC = 0.667), and new RRT (AUC = 0.783) in the validation set. Conclusion: The proposed nomogram holds the potential to estimate the risk of MAKE30 promptly and efficiently in sepsis patients within the initial 24 h of admission, thereby equipping healthcare professionals with valuable insights to facilitate personalized interventions.

Impact of opioid analgesics on the efficacy of immune checkpoint inhibitors in a lung cancer population.

OBJECTIVE: A retrospective clinical study was conducted to compare the prognosis between the opioid analgesic (OA) treated and OA-untreated groups and to evaluate the effect of opioid analgesics on the efficacy of immune checkpoint inhibitors (ICIs) in the treatment of advanced lung cancer patients. In addition, a subgroup analysis of the clinical characteristics of the enrolled patients was performed to explore possible influencing factors. METHODS: This study reviewed the medical records of eligible patients who received ICIs at our institution. The clinicopathological features and clinical outcomes were compared. Also, the use of OA was collected. Patient survival, the incidence of immune-related adverse events (irAEs), and other baseline variables were examined in both cohorts according to whether OA was used. RESULTS: A total of 132 patients were included in the study. Of them, 39 (29.5%) were in the OA-treated group. No significant differences in baseline characteristics were observed between the OA-treated and untreated groups. The combined application of OA treatment significantly shortened progression-free survival (PFS) (P < 0.001) and overall survival (OS) (P = 0.002). However, both groups experienced similar incidences and gradations of irAEs. According to multivariate analysis, OA treatment resulted in significantly worse PFS (HR = 4.994, 95% CI 3.217-7.753, P < 0.001) and OS (HR = 3.618, 95% CI 2.030-6.240, P < 0.001). CONCLUSIONS: Clinical outcomes of ICIs were significantly diminished in a cohort of Chinese patients with advanced lung cancer receiving OA therapy.

GATA3/long noncoding RNA MHC-R regulates the immune activity of dendritic cells in chronic obstructive pulmonary disease induced by air pollution particulate matter.

Chronic obstructive pulmonary disease (COPD) is a heterogeneous illness associated with aberrant inflammatory immune reaction in the lung in response to noxious particles and gases. Our previous epidemiological studies discovered that long-term exposure to air pollution PM was associated with an increase in the incidence of COPD and lung function decline, but the impact of air pollution on the onset of COPD and its pathogenesis remains obscure. In recent years, long noncoding RNAs (lncRNAs) have been documented to have a crucial role in COPD. Our preliminary study found that the expression of lncRNA MHC-R in the lung tissues of rats exposed to air pollution PM was dramatically elevated, and the specific expression was mainly focused on the immune-related MHC I, antigen-presenting, and adaptive immune response. After transcription factor prediction, it was found that GATA3 could be combined with the specific sequence of the lncRNA MHC-R promoter region. Dendritic cells (DCs) are necessary antigen-presenting cells (APCs) with the most potent antigen-presenting function. We proved that GATA3/lncRNA MHC-R might regulate the immune activities of DCs to participate in the pathogenic mechanism of COPD induced by air pollution PM, which opens up a new way for early COPD diagnosis and treatment.

Diagnosis of Esophageal Lesions by Multi-Classification and Segmentation Using an Improved Multi-Task Deep Learning Model.

It is challenging for endoscopists to accurately detect esophageal lesions during gastrointestinal endoscopic screening due to visual similarities among different lesions in terms of shape, size, and texture among patients. Additionally, endoscopists are busy fighting esophageal lesions every day, hence the need to develop a computer-aided diagnostic tool to classify and segment the lesions at endoscopic images to reduce their burden. Therefore, we propose a multi-task classification and segmentation (MTCS) model, including the Esophageal Lesions Classification Network (ELCNet) and Esophageal Lesions Segmentation Network (ELSNet). The ELCNet was used to classify types of esophageal lesions, and the ELSNet was used to identify lesion regions. We created a dataset by collecting 805 esophageal images from 255 patients and 198 images from 64 patients to train and evaluate the MTCS model. Compared with other methods, the proposed not only achieved a high accuracy (93.43%) in classification but achieved a dice similarity coefficient (77.84%) in segmentation. In conclusion, the MTCS model can boost the performance of endoscopists in the detection of esophageal lesions as it can accurately multi-classify and segment the lesions and is a potential assistant for endoscopists to reduce the risk of oversight.

Biomass-related PM2.5 induces mitochondrial fragmentation and dysfunction in human airway epithelial cells.

The use of biomass for cooking and heating is considered an important factor associated with chronic obstructive pulmonary disease (COPD), but few studies have previously addressed its underlying mechanisms. Therefore, this research aimed to evaluate the effects of biomass-related PM2.5 (BRPM2.5) exposure on 16HBE human airway epithelial cells and in mice with regard to mitochondrial dysfunction. Our study indicated that BRPM2.5 exposure of 16HBE cells resulted in mitochondrial dysfunction, including decreased mitochondrial membrane potential, increased expression of fission proteins-phospho-DRP1, increased mitochondrial ROS (mtROS), and decreased levels of ATP. BRPM2.5 altered the mitochondrial metabolism of 16HBE cells by decreasing mitochondrial oxygen consumption and glycolysis. However, Mitochondria targeted peptide SS-31 eliminated mitochondrial ROS and alleviated the ATP deficiency and proinflammatory cytokines release. BRPM2.5 exposure resulted in abnormal mitochondrial morphological alterations both in 16HBE and in lung tissue. Taken together, these results suggest that BRPM2.5 has detrimental effects on human airway epithelial cells, leading to mitochondrial dysfunction, abnormal mitochondrial metabolism and altered mitochondrial dynamics. The present study provides the first evidence that disruption of mitochondrial structure and mitochondrial metabolism may be one of the mechanisms of BRPM2.5-induced respiratory dysfunction.

Dual carbon and void space confined SiOx/C@void@Si/C yolk-shell nanospheres with high-rate performances and outstanding cyclability for lithium-ion batteries anodes.

Silicon-based anode materials with high theoretical capacity have great challenges of enormous volume expansion and poor electronic conductivity. Herein, a novel dual carbon confined SiOx/C@void@Si/C yolk-shell monodisperse nanosphere with void space have been fabricated through hydrothermal reaction, carbonization, and in-situ low-temperature aluminothermic reduction. Furthermore, the O/Si ratio and void space between SiOx/C core and Si/C shell can be effectively tuned by the length of aluminothermic reduction time. The SiOx/C core plays a role of maintaining the spherical structure and the void space can accommodate the volume expansion of Si. Moreover, the inner and outer carbons not only alleviate volume variation of SiOx and Si but also enhance the electrical conductivity of composites. Benefiting from the synergy of the double carbon and void space, the optimized VSC-14 anode affords prominent cycle stability with reversible capacity of 1094 mAh g-1 after 550 cycles at 200 mA g-1. By pre-lithiation treatment, the VSC-14 achieves an initial Coulombic efficiency of 93.27% at 200 mA g-1 and a reversible capacity of 348 mAh g-1 at 5 A g-1 after 4000 cycles. Furthermore, the pouch cell using VSC-14 anode and LiFePO4 cathode delivers a reversible capacity of 138 mAh g-1 at 0.2C. We hope this strategy can provide a scientific method to synthesis yolk-shell Si-based materials.

A Covalent Heterostructure of Metal Phosphide Quantum Dots Anchored in N, P Co-doped Carbon Nanocapsules for Fast and Durable Lithium Storage.

Transition-metal phosphides (TMPs) anodes for lithium ion batteries (LIBs) usually show poor rate capability and rapid capacity degradation owing to their low electronic conductivities, huge volumetric changes, as well as inferior reversibility of the discharge product Li3P. Herein, a covalent heterostructure with TMPs quantum dots anchored in N, P co-doped carbon nanocapsules (NPC) has been prepared in which the P element in TMPs is simultaneously doped into the carbon matrix. As a proof of concept, Co2P quantum dots covalently anchored in NPC (Co2P QDs/NPC) is prepared and evaluated as an anode for LIBs. The Co2P QDs/NPC electrode not only demonstrates a high capacity and an extraordinary rate performance but also delivers an impressive cyclability with a high capacity retention of 102.5% after 1600 cycles, one of the best reported values for TMPs-based electrode materials for LIBs. The covalent heterostructure can facilitate the electron/ion transfer and maintain the structural stability during the intensive cycles. Moreover, density functional theory calculations demonstrate that the interfacial covalent coupling can enhance the electrochemical reversibility of the discharge product Li3P in the charge processes via lowering the conversion reaction energies. This work presents an effective interfacial engineering strategy for developing high-performance TMPs anodes for advanced LIBs.

Therapeutic Targeting of Cancer: Epigenetic Homeostasis.

A large number of studies have revealed that epigenetics plays an important role in cancer development. However, the currently-developed epigenetic drugs cannot achieve a stable curative effect. Thus, it may be necessary to redefine the role of epigenetics in cancer development. It has been shown that embryonic development and tumor development share significant similarities in terms of biological behavior and molecular expression patterns, and epigenetics may be the link between them. Cell differentiation is likely a manifestation of epigenetic homeostasis at the cellular level. In this article, we introduced the importance of epigenetic homeostasis in cancer development and analyzed the shortcomings of current epigenetic treatment regimens. Understanding the dynamic process of epigenetic homeostasis in organ development can help us characterize cancer according to its differentiation stages, explore new targets for cancer treatment, and improve the clinical prognosis of patients with cancer.

Vanadium Nitride Quantum Dots/Holey Graphene Matrix Boosting Adsorption and Conversion Reaction Kinetics for High-Performance Lithium-Sulfur Batteries.

Lithium-sulfur batteries (LSBs) have been considered as potential next-generation energy storage systems due to their high specific energy of 2600 Wh kg-1 and 2800 Wh L-1. Nevertheless, the practical application of LSBs still faces several hazards, including the shuttle effect of soluble lithium polysulfides, low electrical conductivities of solid sulfur and lithium sulfides, and large volume expansion during charge/discharge cycles. To address this critical challenge, we innovatively proposed facile synthesis of nanostructured VN quantum dots (VNQD)/holey graphene matrix for stabilizing the sulfur cathode by simultaneously promoting the trapping, anchoring, and catalyzing efficiencies of both LiPSs and Li2S. Benefiting from abundant edge catalytic sites of VNQD, in-plane nanopores of graphene, and high electrical conductivity, the sulfur host not only provides high adsorption capability toward soluble polysulfides, strong binding ability for anchoring solid Li2S, and their rapid conversion kinetics but also contributes abundant sulfur storage sites and efficient transport pathways for lithium ions (Li+) and electrons. Consequently, the sulfur cathode exhibits high initial capacities of 1320 mAh g-1, high rate capability (850 mAh g-1 @ 4 mA cm-2), and high capacity retention of 99.95% per cycle after 500 cycles, providing a feasible solution for the practical utilization of shuttle-free Li-S batteries.

Multi-Task Model for Esophageal Lesion Analysis Using Endoscopic Images: Classification with Image Retrieval and Segmentation with Attention.

The automatic analysis of endoscopic images to assist endoscopists in accurately identifying the types and locations of esophageal lesions remains a challenge. In this paper, we propose a novel multi-task deep learning model for automatic diagnosis, which does not simply replace the role of endoscopists in decision making, because endoscopists are expected to correct the false results predicted by the diagnosis system if more supporting information is provided. In order to help endoscopists improve the diagnosis accuracy in identifying the types of lesions, an image retrieval module is added in the classification task to provide an additional confidence level of the predicted types of esophageal lesions. In addition, a mutual attention module is added in the segmentation task to improve its performance in determining the locations of esophageal lesions. The proposed model is evaluated and compared with other deep learning models using a dataset of 1003 endoscopic images, including 290 esophageal cancer, 473 esophagitis, and 240 normal. The experimental results show the promising performance of our model with a high accuracy of 96.76% for the classification and a Dice coefficient of 82.47% for the segmentation. Consequently, the proposed multi-task deep learning model can be an effective tool to help endoscopists in judging esophageal lesions.

Enhancing Adsorption and Reaction Kinetics of Polysulfides Using CoP-Coated N-Doped Mesoporous Carbon for High-Energy-Density Lithium-Sulfur Batteries.

Lithium-sulfur (Li-S) batteries have shown great potential in the next-generation energy storage devices due to high theoretical energy density and low cost. To obtain high-performance Li-S batteries, it is important to inhibit the polysulfide shuttle effect and improve the reaction kinetics of polysulfides. Herein, CoP nanoparticles coated by metal-organic framework-derived N-doped mesoporous carbon (CoP@N-C) composites are synthesized and applied in both a cathode for a sulfur host and a modified layer on a separator for high-energy-density Li-S batteries since the CoP component has strong chemical anchoring capability toward soluble polysulfides and high electrochemical activity toward polysulfides transformation. Meanwhile, the porous structure of conductive N-doped mesoporous carbon can not only buffer the volume variation of sulfur during the charge/discharge process but also enhance the charge transport rate in the cathode. The constructed batteries have demonstrated a high specific capacity of 1222 mAh g-1 (8.6 mAh cm-2) with a high sulfur areal loading of ∼7.0 mg cm-2 on cathodes, and a mass loading of 0.35 mg cm-2 for modified layer on separators. Its average capacity decay is only 0.076% per cycle after 100 cycles. This work presents the highly competitive performance of Li-S batteries on the areal capacity and capacity decay.

Strong adsorption of tetracycline hydrochloride on magnetic carbon-coated cobalt oxide nanoparticles.

The overuse of antibiotics, including tetracycline hydrochloride (TC), seriously threatens human health and ecosystems. In this work, magnetic carbon-coated cobalt oxide nanoparticles (CoO@C) were prepared by one-step annealing method and used as an adsorbent for efficient removal of TC from aqueous solution. The characteristic of the materials was studied by SEM, TEM, and XRD, revealing CoO nanoparticles (≤10 nm) were coated by carbon layer. Several influencial parameters, such as annealing temperature and pH on adsorption of TC, were explored, and found that the maximum adsorption capacity of CoO@C on TC reached as high as 769.43 mg g-1. Furthermore, CoO@C displayed excellent stability and reusability. After four repeated use of the adsorbent, the adsorption capacity still remained at 90% of the initial capacity. The pseudo-second order model and Temkin model proved that it was an exothermic chemical adsorption process. Furthermore, after analysis of FT-IR, Zeta-potential, XPS, the positive charge on the surface of CoO@C forms a strong electrostatic interaction with TC, and in addition, a surface bond is formed between the adsorbent and the TC molecule. This work provides a novel and efficient adsorbent for the purification of TC-containing wastewater.

Rational Design and Controllable Synthesis of Multishelled Fe2O3@SnO2@C Nanotubes as Advanced Anode Material for Lithium-/Sodium-Ion Batteries.

Hierarchical Fe2O3 and SnO2 nanostructures have shown great potential for applications in high-performance ion batteries because of their superiority, including wide resources, facile preparation, environmental friendliness, and high energy density. However, some severe challenges, such as rapid capacity decay due to volume expansion upon cycling and poor conductivity, limit their rate performance. To address this issue, multishelled Fe2O3@SnO2@C (FSC) nanotubes were designed and synthesized by using a template method and Ostwald interaction. The as-prepared FSC nanotubes can deliver a high capacity of 1659 mA h g-1 at a current density of 200 mA g-1 and a high reversible capacity of 818 mA h g-1 at 2000 mA g-1 for lithium-ion batteries. Particularly, a high specific capacity of 1024 mA h g-1 is still maintained after 100 charging/discharging cycles at 200 mA g-1. Applied in sodium-ion batteries, the multishelled FSC nanotubes manifest a high specific capacity of 449 mA h g-1 after 180 cycles at 50 mA g-1. Such excellent performances of the as-fabricated FSC nanotubes may be due to the unique multishelled tubular structure, porous characteristics, and high specific surface area. Therefore, the present work provides an outstanding method to improve the energy storage performance of metal oxide composites and other types of nanocomposites.

Role of TMPRSS4 Modulation in Breast Cancer Cell Proliferation.

Background: TMPRSS4 is a novel Type II transmembrane serine protease found at the surface of the cells and is involved in the development and cancer progression. However, TMPRSS4 functions in breast cancer remain poor understand. The present study investigated the function of TMPRSS4 in the breast cancer cells and the potential mechanistic action underling. Materials and Methods: The lentiviral vectors causing TMPRSS4 down-regulation and over-expression were established and transfected in MDA-MB-468 and MCF-7 cells, respectively. By using the CCK- 8 assay, cell proliferation was analyzed. Moreover, western blot was used to detect the expression of certain proteins related to cell apoptosis (Bax and Bcl2) signaling pathway and telomere maintenance (POT1, TPP1, and UBE2D3). Cell cycle and cell apoptosis were also analyzed by using the Flow cytometry analysis. TMPRSS4 expression was detected at the mRNA level and protein level by performing qPCR and western blot technique, respectively. Results: TMPRSS4 expression is inhibited in stable transfected MDA-MB-468-shTMPRSS4 cells compared to the control MDA-MB-468-NC and its expression is up-regulated in stable transfected MCF-7-TMPTSS4 compared to its control MCF-7-NC. Moreover, TMPRSS4 silencing in breast cancer reduces cells proliferation by promoting cell cycle arrest in G2/M phase, cell apoptosis, and telomere maintenance impairment while the TMPRSS4 overexpression increases cells proliferation through cell apoptosis reduction and telomere maintenance reinforcement associated with insignificant change in cell cycle progression. Conclusion: TMPRSS4 plays important roles in cancer progression and may be considered as a good therapeutic target for cancer gene therapy especially breast cancer.

Primary retroperitoneal liposarcoma with extensive ossification: A case report.

RATIONALE: Primary retroperitoneal liposarcoma, which originates from mesenchymal tissues, can rarely present with extensive ossification. PATIENT CONCERNS: A 41-year-old male patient presented with a chief complaint of discomfort around the waist for 2 months. DIAGNOSES: Computerized tomography (CT) and magnetic resonance imaging suggested a lesion of approximately 5.6 × 5.1 × 8.7 cm in front of the psoas major muscle, which was considered to be a mesenchymal or neurogenic tumor. INTERVENTIONS: The hard mass was removed by laparotomy, and the pathological investigation revealed that this was an atypical lipomatous tumor/well-differentiated liposarcoma, with extensive ossification. OUTCOMES: The patient was discharged from the hospital after surgery. There was no sign of reoccurrence after 1 year of follow-up. LESSONS: Retroperitoneal liposarcomas with extensive ossification are rare tumors that can present with nonspecific symptoms, and are difficult to diagnose. CT is the most common imaging technique, and surgical resection has been considered to be the most effective treatment. This rare case can be challenging for diagnosis and treatment.