Multidrug-resistant Klebsiella Pneumoniae Infection Led to Resection of the Graft in a Small Bowel Transplant Recipient: A Case Report and Review of the Literature

BACKGROUND

Infection due to multidrug-resistant Klebsiella pneumoniae is a common cause of graft resection after small bowel transplantation. We report a failed case in which the intestinal graft was resected 18 days after the operation due to postoperative infection with multidrug-resistant K pneumoniae and a literature review of other common causes of small bowel transplantation failure have been reported.

METHODS

A female, 29 years of age, underwent partial living small bowel transplantation for short bowel syndrome. After the operation, the patient was infected with multidrug-resistant K pneumoniae, even though various anti-infective regimens were employed. It further developed into sepsis and disseminated into intravascular coagulation, leading to exfoliation and necrosis of the intestinal mucosa. Finally, the intestinal graft had to be resected to save the patient's life.

RESULTS

Multidrug-resistant K pneumoniae infection often affects the biological function of intestinal grafts and can even lead to necrosis. Other common causes of failure, including postoperative infection, rejection, post-transplantation lymphoproliferative disorder, graft-vs-host disease, surgical complications, and other related diseases, were also discussed throughout the literature review.

CONCLUSIONS

Pathogenesis due to diverse and interrelated factors makes the survival of intestinal allografts a great challenge. Therefore, only by fully understanding and mastering the common causes of surgical failure can the success rate of small bowel transplantation be effectively improved.

Deep learning radio-clinical signature for predicting neoadjuvant chemotherapy response and prognosis from pretreatment CT images of locally advanced gastric cancer patients

BACKGROUND

Early noninvasive screening of patients who would benefit from neoadjuvant chemotherapy (NCT) is essential for personalized treatment of locally advanced gastric cancer (LAGC). The aim of this study was to identify a radioclinical signatures from pretreatment oversampled CT images to predict the response to NCT and prognosis of LAGC patients.

METHODS

LAGC patients were retrospectively recruited from six hospitals from January 2008 to December 2021. An SE-ResNet50-based chemotherapy response prediction system was developed from pretreatment CT images preprocessed with an imaging oversampling method (i.e., DeepSMOTE). Then, the Deep learning (DL) signature and clinic-based features were fed into the deep learning radioclinical signature (DLCS). The predictive performance of the model was evaluated based on discrimination, calibration and clinical usefulness. An additional model was built to predict overall survival (OS) and explore the survival benefit of the proposed DL signature and clinicopathological characteristics.

RESULT

A total of 1060 LAGC patients were recruited from six hospitals, the training cohort (TC) and internal validation cohort (IVC) patients were randomly selected from center I. An external validation cohort (EVC) of 265 patients from 5 other centers was also included. The DLCS exhibited excellent performance in predicting the response to NCT in the IVC (AUC, 0.86) and EVC (AUC, 0.82), with good calibration in all cohorts (P>0.05). Moreover, the DLCS model outperformed the clinical model (P<0.05). Additionally, we found that the DL signature could serve as an independent factor for prognosis (HR, 0.828, P=0.004). The C-index, iAUC, and IBS for the OS model were 0.64, 1.24 and 0.71 in the test set.

CONCLUSION

We proposed a DLCS model that combined imaging features with clinical risk factors to accurately predict tumor response and identify the risk of OS in LAGC patients priors to NCT that can then be used to guide personalized treatment plans with the help of computerized tumor-level characterization.

Prediction of recurrence of ischemic stroke within 1 year of discharge based on machine learning MRI radiomics

Purpose

This study aimed to investigate the value of a machine learning-based magnetic resonance imaging (MRI) radiomics model in predicting the risk of recurrence within 1 year following an acute ischemic stroke (AIS).

Methods

The MRI and clinical data of 612 patients diagnosed with AIS at the Second Affiliated Hospital of Nanchang University from March 1, 2019, to March 5, 2021, were obtained. The patients were divided into recurrence and non-recurrence groups according to whether they had a recurrent stroke within 1 year after discharge. Randomized splitting was used to divide the data into training and validation sets using a ratio of 7:3. Two radiologists used the 3D-slicer software to label the lesions on brain diffusion-weighted (DWI) MRI sequences. Radiomics features were extracted from the annotated images using the pyradiomics software package, and the features were filtered using the Least Absolute Shrinkage and Selection Operator (LASSO) regression analysis. Four machine learning algorithms, logistic regression (LR), Support Vector Classification (SVC), LightGBM, and Random forest (RF), were used to construct a recurrence prediction model. For each algorithm, three models were constructed based on the MRI radiomics features, clinical features, and combined MRI radiomics and clinical features. The sensitivity, specificity, and area under the receiver operating characteristic (ROC) curve (AUC) were used to compare the predictive efficacy of the models.

Results

Twenty features were selected from 1,037 radiomics features extracted from DWI images. The LightGBM model based on data with three different features achieved the best prediction accuracy from all 4 models in the validation set. The LightGBM model based solely on radiomics features achieved a sensitivity, specificity, and AUC of 0.65, 0.671, and 0.647, respectively, and the model based on clinical data achieved a sensitivity, specificity, and AUC of 0.7, 0.799, 0.735, respectively. The sensitivity, specificity, and AUC of the LightGBM model base on both radiomics and clinical features achieved the best performance with a sensitivity, specificity, and AUC of 0.85, 0.805, 0.789, respectively.

Conclusion

The ischemic stroke recurrence prediction model based on LightGBM achieved the best prediction of recurrence within 1 year following an AIS. The combination of MRI radiomics features and clinical data improved the prediction performance of the model.

Cancer Stem Cells are Actually Stem Cells with Disordered Differentiation: the Monophyletic Origin of Cancer

Cancer stem cells (CSCs) play an important role in cancer development. Based on advancements in CSC research, we propose a monophyletic model of cancer. This model is based on the idea that CSCs are stem cells with disordered differentiation whose original purpose was to repair damaged tissues. Inflammatory responses and damage repair signals are crucial for the creation and maintenance of CSCs. Normal quiescent stem cells are activated by environmental stimulation, such as an inflammatory response, and undergo cell division and differentiation. In the initial stage of cancer development, stem cell differentiation leads to heteromorphism due to the accumulation of gene mutations, resulting in the development of metaplasia or precancerosis. In the second stage, accumulated mutations induce poor differentiation and lead to cancer development. The monophyletic model illustrates the evolution, biological behavior, and hallmarks of CSCs, proposes a concise understanding of the origin of cancer, and may encourage a novel therapeutic approach.

[Clinical value of nomogram model in evaluating the prognosis of cholangiocarcinoma after interventional therapy]

Objective: To investigate the clinical value and efficacy of the nomogram model in evaluating the prognosis of cholangiocarcinoma after interventional therapy. Methods: The clinical data of 259 patients with cholangiocarcinoma who received interventional therapy at the First Affiliated Hospital of zhengzhou University from January 2014 to June 2021 were retrospectively analyzed, including 148 males and 111 females, aged from 26 to 91 (65±12) years. They were randomly divided into a training group (181 cases) and a validation group (78 cases) in a ratio of 7∶3. Cox regression analysis was performed in the training group, independent risk factors affecting the prognosis of patients were screened, and a nomogram for 6-month, 1-year, and 2-year survival was constructed. The performance of the nomogram was analyzed by calculating the area under the receiver operating characteristic curve (AUC) value, calibration curve, and decision curve, and the predictive efficacy of the model was evaluated in the validation group. Results: There was no significant difference in baseline data between the training group and the validation group, which was comparable. Regression analysis showed that T stage (T2: HR=0.147,95%CI: 0.077-0.281;T3: HR=0.207,95%CI: 0.122-0.351;T4: HR=0.864,95%CI: 0.537-1.393), tumor diameter (17-33 mm: HR=0.201,95%CI: 0.119-0.341;≥33 mm: HR=0.795,95%CI: 0.521-1.211) and differentiation degree(middle differentiation: HR=3.318,95%CI: 2.082-5.289;highly differentiation: HR=1.842,95%CI: 1.184-2.867) were risk factors affecting the prognosis of interventional therapy for cholangiocarcinoma. The AUC values of the survival curve prediction models were generally consistent between the training and validation groups, and the AUC values of the training group at 6 months, 1 year, and 2 years were 0.925 (95%CI: 0.888-0.963), 0.921 (95%CI: 0.877-0.964) and 0.974 (95%CI: 0.957-0.993), respectively. In the validation group, the 6-month, 1-year, and 2-year AUC values were 0.951 (95%CI: 0.911-0.991), 0.917 (95%CI: 0.857-0.977) and 0.848 (95%CI: 0.737-0.959), respectively, and the AUC values were all greater than 0.8, suggesting that the nomogram had better discrimination ability. The calibration curves of the prediction models of the two groups were basically consistent, and the shape of the calibration curves at 6 months and 1 year fitted the ideal curve, while the fitting degree of the calibration curves at 2 years was relatively poor. The decision curve showed the high clinical utility of this nomogram in predicting the 6-month, 1-year survival of patients with cholangiocarcinoma. Conclusions: T stage, tumor diameter, and differentiation are independent risk factors affecting the prognosis of patients with interventional cholangiocarcinoma, and the nomogram model proposed in this study has good distinguishing ability and exact clinical value for prognosis evaluation.

High-Conductivity Chalcogenide Glasses in Ag-Ga2Te3-SnTe Systems and Their Suitability as Thermoelectric Materials

A novel high-conductivity Ag_x[(Ga₂Te₃)₃₄(SnTe)₆₆]_100-x tellurium-based glassy system was fabricated via melt spinning with the glass formation area in the range of x = 0-15 mol %. A bulk Ag₁₀[(Ga₂Te₃)₃₄(SnTe)₆₆]₉₀ glass (A10) was obtained via spark plasma sintering at 450 K using a 5 min dwell time and 400 MPa pressure. The fabricated A10 glass exhibited higher room-temperature conductivity (σ_300 K = 46 S m^-1), larger glass transition temperature (T_g = 482 K), and ultralower thermal conductivity (∼0.19 W m^-1 K^-1) compared to those of previously reported Cu-Ge-Te, Cu-As-Te, Cu-Ge-As-Te, and Cu-As-Se-Te glassy systems with the approximate doping concentrations of 5-20%, demonstrating that this distinctive Ag-Ga₂Te₃-SnTe system is interesting materials for thermoelectric applications. The high-conductivity Ag-Ga₂Te₃-SnTe glassy system will extend investigations into similar glassy semiconductors and also can be used for preparing glass ceramics with potential applications in other fields.

Octahedral Distortion and Displacement-Type Ferroelectricity with Switchable Photovoltaic Effect in a 3d^{3}-Electron Perovskite System

Because of the half-filled t_{2g}-electron configuration, the BO_{6} octahedral distortion in a 3d^{3} perovskite system is usually very limited. In this Letter, a perovskitelike oxide Hg_{0.75}Pb_{0.25}MnO_{3} (HPMO) with a 3d^{3} Mn^{4+} state was synthesized by using high pressure and high temperature methods. This compound exhibits an unusually large octahedral distortion enhanced by approximately 2 orders of magnitude compared with that observed in other 3d^{3} perovskite systems like RCr^{3+}O_{3} (R=rare earth). Essentially different from centrosymmetric HgMnO_{3} and PbMnO_{3}, the A-site doped HPMO presents a polar crystal structure with the space group Ama2 and a substantial spontaneous electric polarization (26.5  μC/cm^{2} in theory) arising from the off-center displacements of A- and B-site ions. More interestingly, a prominent net photocurrent and switchable photovoltaic effect with a sustainable photoresponse were observed in the current polycrystalline HPMO. This Letter provides an exceptional d^{3} material system which shows unusually large octahedral distortion and displacement-type ferroelectricity violating the "d^{0}-ness" rule.

The Effect of Key Electronic States on Excess Lithium Intercalation in Li2RuyMn1-yO3

Excess reversible lithium storage is an alternative crucial strategy besides the expansion of redox centers to boost the capacity of layered cathodes. However, the mechanism of excess Li⁺ intercalation is far from being comprehended, indisputably hindering the development of layered cathodes. Herein, the comparative study of Li₂Ru_yMn_1-yO₃ and Li₂Ru_yTi_1-yO₃ by X-ray absorption and photoemission spectroscopies attempts to illustrate the origin. The charge transfer from Ru to Mn through TM-O π bonding interaction with the formation of O holes has been revealed in Li₂Ru_yMn_1-yO₃, which originates from the inductive effect and the approaching energy level of Mn and Ru bands. The electronic state is thought to reduce the Coulomb repulsion of Li⁺ with the matrix, promoting excess Li⁺ intercalation. The results are instructive to the rational design of layered cathodes to achieve a larger reversible capacity in a wide voltage window.

Evaluation of antibody kinetics and durability in health individuals vaccinated with inactivated COVID-19 vaccine (CoronaVac): a cross-sectional and cohort study in Zhejiang, China

Background: Although inactivated COVID-19 vaccines are proven to be safe and effective in the general population, the dynamic response and duration of antibodies after vaccination in the real world should be further assessed.

Methods: We enrolled 1067 volunteers who had been vaccinated with one or two doses of CoronaVac in Zhejiang Province, China. Another 90 healthy adults without previous vaccinations were recruited and vaccinated with three doses of CoronaVac, 28 days and 6 months apart. Serum samples were collected from multiple timepoints and analyzed for specific IgM/IgG and neutralizing antibodies (NAbs) for immunogenicity evaluation. Antibody responses to the Delta and Omicron variants were measured by pseudovirus-based neutralization tests.

Results: Our results revealed that binding antibody IgM peaked 14-28 days after one dose of CoronaVac, while IgG and NAbs peaked approximately 1 month after the second dose then declined slightly over time. Antibody responses had waned by month 6 after vaccination and became undetectable in the majority of individuals at 12 months. Levels of NAbs to live SARS-CoV-2 were correlated with anti-SARS-CoV-2 IgG and NAbs to pseudovirus, but not IgM. Homologous booster around 6 months after primary vaccination activated anamnestic immunity and raised NAbs 25.5-fold. The neutralized fraction subsequently rose to 36.0% for Delta (p=0.03) and 4.3% for Omicron (p=0.004), and the response rate for Omicron rose from 7.9% (7/89) to 17.8% (16/90).

Conclusions: Two doses of CoronaVac vaccine resulted in limited protection over a short duration. The inactivated vaccine booster can reverse the decrease of antibody levels to prime strain, but it does not elicit potent neutralization against Omicron; therefore, the optimization of booster procedures is vital.

Funding: Key Research and Development Program of Zhejiang Province; Key Program of Health Commission of Zhejiang Province/ Science Foundation of National Health Commission; Major Program of Zhejiang Municipal Natural Science Foundation; Explorer Program of Zhejiang Municipal Natural Science Foundation.

Constructing a novel mitochondrial-related gene signature for evaluating the tumor immune microenvironment and predicting survival in stomach adenocarcinoma

BACKGROUND

The incidence and mortality of gastric cancer ranks fifth and fourth worldwide among all malignancies, respectively. Accumulating evidences have revealed the close relationship between mitochondrial dysfunction and the initiation and progression of stomach cancer. However, rare prognostic models for mitochondrial-related gene risk have been built up in stomach cancer.

METHODS

In current study, the expression and prognostic value of mitochondrial-related genes in stomach adenocarcinoma (STAD) patients were systematically analyzed to establish a mitochondrial-related risk model based on available TCGA and GEO databases. The tumor microenvironment (TME), immune cell infiltration, tumor mutation burden, and drug sensitivity of gastric adenocarcinoma patients were also investigated using R language, GraphPad Prism 8 and online databases.

RESULTS

We established a mitochondrial-related risk prognostic model including NOX4, ALDH3A2, FKBP10 and MAOA and validated its predictive power. This risk model indicated that the immune cell infiltration in high-risk group was significantly different from that in the low-risk group. Besides, the risk score was closely related to TME signature genes and immune checkpoint molecules, suggesting that the immunosuppressive tumor microenvironment might lead to poor prognosis in high-risk groups. Moreover, TIDE analysis demonstrated that combined analysis of risk score and immune score, or stromal score, or microsatellite status could more effectively predict the benefit of immunotherapy in STAD patients with different stratifications. Finally, rapamycin, PD-0325901 and dasatinib were found to be more effective for patients in the high-risk group, whereas AZD7762, CEP-701 and methotrexate were predicted to be more effective for patients in the low-risk group.

CONCLUSIONS

Our results suggest that the mitochondrial-related risk model could be a reliable prognostic biomarker for personalized treatment of STAD patients.

Conductive Polyaniline Particles Regulating In Vitro Hydrolytic Degradation and Erosion of Hydroxyapatite/Poly(lactide-co-glycolide) Porous Scaffolds for Bone Tissue Engineering

In addition to biocompatibility and bioactivity, scaffolds with superior bone tissue regenerative capacity should possess excellent functionality (e.g., electroactivity and conductivity) and biodegradability matching with the rate of bone reconstruction. However, current conductive scaffolds display a reduced biodegradability rate and weakened biocompatibility. In this study, injectable conductive porous scaffolds were fabricated, incorporating camphor sulfonic acid-doped polyaniline (PANI) into hydroxyapatite/poly(lactide-co-glycolide) (HA/PLGA) scaffolds, using solvent-casting/particulate-leaching methodology. These scaffolds demonstrated excellent electroactivity, conductivity, hydrophilicity, thermodynamic properties, antibacterial properties, and biocompatibility. Their degradation behavior was explored by regulating the PANI content. The results demonstrated that adding an appropriate content of PANI would increase the pore size, porosity, and water absorption of the conductive scaffold and promote the formation of filamentous fiber byproducts with acidic hydrolysates, which accelerated the degradation rate of the scaffold. Owing to π-π stacking and hydrogen bonding, the conductive scaffold with 10 wt % PANI efficiently retarded the decrease in the thermal and mechanical properties of the scaffolds during a 16 week degradation. Thus, better regulation of degradation behavior and correlation would allow conductive porous scaffolds, such as bone implants, to achieve better bone ingrowth and restoration.

Anemonin reduces hydrogen peroxide-induced oxidative stress, inflammation and extracellular matrix degradation in nucleus pulposus cells by regulating NOX4/NF-κB signaling pathway

BACKGROUND

Excessive oxidative stress plays a critical role in the progression of various diseases, including intervertebral disk degeneration (IVDD). Recent studies have found that anemonin (ANE) possesses antioxidant and anti-inflammatory effects. However, the role of ANE in IVDD is still unclear. Therefore, this study investigated the effect and mechanism of ANE on H₂O₂ induced degeneration of nucleus pulposus cells (NPCs).

METHODS

NPCs were pretreated with ANE, and then treated with H₂O₂. NOX4 was upregulated by transfection of pcDNA-NOX4 into NPCs. Cytotoxicity was detected by MTT, oxidative stress-related indicators and inflammatory factors were measured by ELISA, mRNA expression was assessed by RT-PCR, and protein expression was tested by western blot.

RESULTS

ANE attenuated H₂O₂-induced inhibition of NPCs activity. H₂O₂ enhanced oxidative stress, namely, increased ROS and MDA levels and decreased SOD level. However, these were suppressed and pretreated by ANE. ANE treatment repressed the expression of inflammatory factors (IL-6, IL-1β and TNF-α) in H₂O₂-induced NPCs. ANE treatment also prevented the degradation of extracellular matrix induced by H₂O₂, showing the downregulation of MMP-3, 13 and ADAMTS-4, 5 and the upregulation of collagen II. NOX4 is a key factor regulating oxidative stress. Our study confirmed that ANE could restrain NOX4 and p-NF-κB. In addition, overexpression of NOX4 counteracted the antioxidant and anti-inflammatory activities of ANE in H₂O₂-induced NPCs, and the inhibition of the degradation of extracellular matrix induced by ANE was also reversed by overexpression of NOX4.

CONCLUSION

ANE repressed oxidative stress, inflammation and extracellular matrix degradation in H₂O₂-induced NPCs by inhibiting NOX4/NF-κB pathway. Our study indicated that ANE might be a candidate drug for the treatment of IVDD.

Chlorine activation and enhanced ozone depletion induced by wildfire aerosol

Remarkable perturbations in the stratospheric abundances of chlorine species and ozone were observed over Southern Hemisphere mid-latitudes following the 2020 Australian wildfires^1,2. These changes in atmospheric chemical composition suggest that wildfire aerosols affect stratospheric chlorine and ozone depletion chemistry. Here we propose that wildfire aerosol containing a mixture of oxidized organics and sulfate^3-7 increases hydrochloric acid solubility^8-11 and associated heterogeneous reaction rates, activating reactive chlorine species and enhancing ozone loss rates at relatively warm stratospheric temperatures. We test our hypothesis by comparing atmospheric observations to model simulations that include the proposed mechanism. Modelled changes in 2020 hydrochloric acid, chlorine nitrate and hypochlorous acid abundances are in good agreement with observations^1,2. Our results indicate that wildfire aerosol chemistry, although not accounting for the record duration of the 2020 Antarctic ozone hole, does yield an increase in its area and a 3-5% depletion of southern mid-latitude total column ozone. These findings increase concern^2,12,13 that more frequent and intense wildfires could delay ozone recovery in a warming world.

Tumor-Suppressive and Immune-Stimulating Roles of Cholesterol 25-hydroxylase in Pancreatic Cancer Cells

Cholesterol dependence is an essential characteristic of pancreatic ductal adenocarcinoma (PDAC). Cholesterol 25-hydroxylase (CH25H) catalyzes monooxygenation of cholesterol into 25-hydroxycholesterol, which is implicated in inhibiting cholesterol biosynthesis and in cholesterol depletion. Here, we show that, within PDAC cells, accumulation of cholesterol was facilitated by the loss of CH25H. Methylation of the CH25H gene and decreased levels of CH25H expression occurred in human pancreatic cancers and was associated with poor prognosis. Knockout of Ch25h in mice accelerated progression of Kras-driven pancreatic intraepithelial neoplasia. Conversely, restoration of CH25H expression in human and mouse PDAC cells decreased their viability under conditions of cholesterol deficit, and decelerated tumor growth in immune competent hosts. Mechanistically, the loss of CH25H promoted autophagy resulting in downregulation of MHC-I and decreased CD8+ T-cell tumor infiltration. Re-expression of CH25H in PDAC cells combined with immune checkpoint inhibitors notably inhibited tumor growth. We discuss additional benefits that PDAC cells might gain from inactivation of CH25H and the potential translational importance of these findings for therapeutic approaches to PDAC.

IMPLICATIONS

Loss of CH25H by pancreatic cancer cells may stimulate tumor progression and interfere with immunotherapies.

Pyrocumulonimbus affect average stratospheric aerosol composition

Pyrocumulonimbus (pyroCb) are wildfire-generated convective clouds that can inject smoke directly into the stratosphere. PyroCb have been tracked for years, yet their apparent rarity and episodic nature lead to highly uncertain climate impacts. In situ measurements of pyroCb smoke reveal its distinctive and exceptionally stable aerosol properties and define the long-term influence of pyroCb activity on the stratospheric aerosol budget. Analysis of 13 years of airborne observations shows that pyroCb are responsible for 10 to 25% of the black carbon and organic aerosols in the "present-day" lower stratosphere, with similar impacts in both the North and South Hemispheres. These results suggest that, should pyroCb increase in frequency and/or magnitude in future climates, they could generate dominant trends in stratospheric aerosol.

Development and validation of risk prediction models for stroke and mortality among patients with type 2 diabetes in northern China

BACKGROUND

Stroke is one of the leading causes of disability and mortality in patients with type 2 diabetes mellitus (T2DM). Risk models have been developed for predicting stroke and stroke-associated mortality among patients with T2DM. Here, we evaluated risk factors of stroke for individualized prevention measures in patients with T2DM in northern China.

METHODS

In the community-based Tianjin Chronic Disease Cohort study, 58,042 patients were enrolled between January 2014 and December 2019. We used multiple imputation (MI) to impute missing variables and univariate and multivariate Cox's proportional hazard regression to screen risk factors of stroke. Furthermore, we established and validated first-ever prediction models for stroke (Model 1 and Model 2) and death from stroke (Model 3) and evaluated their performance.

RESULTS

In the derivation and validation groups, the area under the curves (AUCs) of Models 1-3 was better at 5 years than at 8 years. The Harrell's C-index for all models was above 0.7. All models had good calibration, discrimination, and clinical net benefit. Sensitivity analysis using the MI dataset indicated that all models had good and stable prediction performance.

CONCLUSION

In this study, we developed and validated first-ever risk prediction models for stroke and death from stroke in patients with T2DM, with good discrimination and calibration observed in all models. Based on lifestyle, demographic characteristics, and laboratory examination, these models could provide multidimensional management and individualized risk assessment. However, the models developed here may only be applicable to Han Chinese.

Non-surgical treatment of giant tumor-like lumbar disc herniation based on enhanced MRI: A case series

RATIONALE

In recent clinical follow-up, it has been vertified that resorption in lumbar disc herniation (LDH) could be of great curative effect in non-surgical treatment for LDH. However, reports of resorption in giant tumor-like LDH are rarely mentioned due to its risk of irreversible neurological damage which could be caused by long-term non-surgical treatment. In our clinical observations, we have found that enhanced MRI helps to distinguish LDH from intradural tumours and to predict the probability of resorption in LDH. We analyzed 8 patients with giant tumor-like LDH who underwent non-surgical treatment, and these patients had resorption during follow-up. All patients were examined with enhanced MRI before treatment, and the type of "bull's eye" sign classification was determined by images. The MRI protrusion volume(VP), resorption rate(HR%) and JOA score of patients at the first visit and the last follow-up were recorded.

PATIENT CONCERNS

8 patients of Han ethnicity were admitted to the department of orthopedic complaining of low back pain for 1week to 12months. They were diagnosed with giant tumor-like LDH by enhanced MRI.

DIAGNOSES

These patients were diagnosed with giant tumor-like LDH.

INTERVENTIONS

We adopted a non-surgical treatment plan for the patients, including taking oral non-steroidal anti-inflammatory agents and performing rehabilitation exercise. In consideration of the risk of irreversible neurological damage, patients were closely observed during treatment and follow-up. Once the following conditions occur, surgical treatment is required immediately: The symptoms are not signifcantly relieved after 3 to 6 months of non-surgical treatment; The symptoms are aggravated by non-surgica treatment; The clinical manifestations of cauda equina syndrome.

OUTCOMES

After treated with oral non-steroidal anti-inflammatory agents and rehabilitation exercise, the resorption was accompanied by clinical symptom relief. No neurological damage occurred in all patients, and the clinical symptoms did not recur in the subsequent follow-up.

LESSONS

Clinicians should fully consider the possibility of resorption prior to surgical treatment in patients with giant LDH. We can predict the probability of resorption in patients with giant LDH based on enhanced MRI. For patients with a high probability of resorption, we can choose non-surgical treatment in the absence of progressive neurological impairment and cauda equina syndrome.

Effects of excess Te on optical and electrical properties of Cd1-xMgxTe single crystals grown by modified vertical Bridgman method

Cd1-xMgxTe crystal is considered as an ideal room temperature radiation detector material due to its excellent opto-electrical properties. In this paper, three large Cd0.95Mg0.05Te ingots with diameter of 30 mm...

Sex Specific Global Burden of Osteoporosis in 204 Countries and Territories, from 1990 to 2030: An Age-Period-Cohort Modeling Study

BACKGROUND

Osteoporosis is a highly prevalent disease with distinct sex pattern. We aimed to estimate the sex specific incidence, prevalence, and disability-adjusted life (DALYs) years of osteoporosis between 1990 and 2019, with additional predictions from 2020 to 2034.

METHODS

We collected osteoporosis disease burden data from the Global Burden of Disease study covering the years 1990 through 2019 in 204 countries and territories. The data included information on the number of incident cases of osteoporosis, DALYs, age-standardized incidence rates (ASIR), age-standardized prevalence rates (ASPR) and age-standardized DALYs rates. Additionally, we performed an age-period-cohort analysis to forecast the burden of osteoporosis.

RESULTS

The global number of incidence cases of osteoporosis, in 2019, reached 41.5 million cases. From 1990 to 2019, the low-middle socio-demographic index (SDI) region had the highest estimated annual percentage change in the world. Compared to males, female's ASIR and ASPR were all about 1.5 times higher than males for the same years in the same SDI regions. The projected global total number of incidence cases for osteoporosis between 2030 and 2034 is estimated to reach 263.2 million (154.4 million for females and 108.8 for males). Additionally, the burden in terms of DALYs is predicted to be 128.7 million (with 78.4 million for females and 50.3 million for males).

CONCLUSION

The global burden of osteoporosis is still increasing, mainly observed in high SDI countries. Females bear a burden 1.5 times higher than males in terms of incidence and DALYs. Steps should be taken to reduce the osteoporosis burden, especially in high SDI countries.

A deep learning model for drug screening and evaluation in bladder cancer organoids

Three-dimensional cell tissue culture, which produces biological structures termed organoids, has rapidly promoted the progress of biological research, including basic research, drug discovery, and regenerative medicine. However, due to the lack of algorithms and software, analysis of organoid growth is labor intensive and time-consuming. Currently it requires individual measurements using software such as ImageJ, leading to low screening efficiency when used for a high throughput screen. To solve this problem, we developed a bladder cancer organoid culture system, generated microscopic images, and developed a novel automatic image segmentation model, AU2Net (Attention and Cross U2Net). Using a dataset of two hundred images from growing organoids (day1 to day 7) and organoids with or without drug treatment, our model applies deep learning technology for image segmentation. To further improve the accuracy of model prediction, a variety of methods are integrated to improve the model's specificity, including adding Grouping Cross Merge (GCM) modules at the model's jump joints to strengthen the model's feature information. After feature information acquisition, a residual attentional gate (RAG) is added to suppress unnecessary feature propagation and improve the precision of organoids segmentation by establishing rich context-dependent models for local features. Experimental results show that each optimization scheme can significantly improve model performance. The sensitivity, specificity, and F1-Score of the ACU2Net model reached 94.81%, 88.50%, and 91.54% respectively, which exceed those of U-Net, Attention U-Net, and other available network models. Together, this novel ACU2Net model can provide more accurate segmentation results from organoid images and can improve the efficiency of drug screening evaluation using organoids.

A novel deep learning segmentation model for organoid-based drug screening

Organoids are self-organized three-dimensional in vitro cell cultures derived from stem cells. They can recapitulate organ development, tissue regeneration, and disease progression and, hence, have broad applications in drug discovery. However, the lack of effective graphic algorithms for organoid growth analysis has slowed the development of organoid-based drug screening. In this study, we take advantage of a bladder cancer organoid system and develop a deep learning model, the res-double dynamic conv attention U-Net (RDAU-Net) model, to improve the efficiency and accuracy of organoid-based drug screenings. In this RDAU-Net model, the dynamic convolution and attention modules are integrated. The feature-extracting capability of the encoder and the utilization of multi-scale information are substantially enhanced, and the semantic gap caused by skip connections has been filled, which substantially improved its anti-interference ability. A total of 200 images of bladder cancer organoids on culture days 1, 3, 5, and 7, with or without drug treatment, were employed for training and testing. Compared with the other variations of the U-Net model, the segmentation indicators, such as Intersection over Union and dice similarity coefficient, in the RDAU-Net model have been improved. In addition, this algorithm effectively prevented false identification and missing identification, while maintaining a smooth edge contour of segmentation results. In summary, we proposed a novel method based on a deep learning model which could significantly improve the efficiency and accuracy of high-throughput drug screening and evaluation using organoids.

Gene expression profiling in nucleus pulposus of human ruptured lumbar disc herniation

Purpose: To examine the differences in gene expression between ruptured and non-ruptured nucleus pulposus tissues of the intervertebral discs using gene chip technology. Methods: A total of 8 patients with nucleus pulposus from a lumbar disc herniation (LDH) undergoing discectomy in our hospital were selected, including 4 ruptured and 4 non-ruptured herniated nucleus pulposus cases. Total RNA was extracted from cells by using TRIzol reagent. Nucleus pulposus cDNA probes of the two groups were obtained by the single marker method and hybridized with a human gene expression profiling chip (Agilent). The fluorescence signal images were scanned by a laser, and the obtained genes were analyzed by bioinformatics. Results: There were 75 differentially expressed genes with more than 2-fold-changes, of which 56 were up-regulated and 19 were down-regulated. The differential expression of THSD7A, which was up-regulated 18 times, was the most significant, followed by CCL5, AQP3 and SDC4. Conclusion: THSD7A can be used as a characteristic differentially expressed gene in human ruptured nucleus pulposus. Moreover, CCL5, AQP3 and SDC4 may improve the chemotaxis of stem cell migration for self-healing of damaged disc tissue, increase water uptake by nucleus accumbens cells, and inhibit the inflammatory response, thus delaying the process of intervertebral disc degeneration.