BAP1 regulates HSF1 activity and cancer immunity in pancreatic cancer.

BACKGROUND: The vast majority of pancreatic cancers have been shown to be insensitive to single-agent immunotherapy. Exploring the mechanisms of immune resistance and implementing combination therapeutic strategies are crucial for PDAC patients to derive benefits from immunotherapy. Deletion of BAP1 occurs in approximately 27% of PDAC patients and is significantly correlated with poor prognosis, but the mechanism how BAP1-deletion compromises survival of patients with PDAC remain a puzzle. METHODS: Bap1 knock-out KPC (KrasG12D/+; LSLTrp53R172H/+; Pdx-1-Cre) mice and control KPC mice, syngeneic xenograft models were applied to analysis the correlation between BAP1 and immune therapy response in PDAC. Immunoprecipitation, RT-qPCR, luciferase and transcriptome analysis were combined to revealing potential mechanisms. Syngeneic xenograft models and flow cytometry were constructed to examine the efficacy of the inhibitor of SIRT1 and its synergistic effect with anti-PD-1 therapy. RESULT: The deletion of BAP1 contributes to the resistance to immunotherapy in PDAC, which is attributable to BAP1's suppression of the transcriptional activity of HSF1. Specifically, BAP1 competes with SIRT1 for binding to the K80 acetylated HSF1. The BAP1-HSF1 interaction preserves the acetylation of HSF1-K80 and promotes HSF1-HSP70 interaction, facilitating HSF1 oligomerization and detachment from the chromatin. Furthermore, we demonstrate that the targeted inhibition of SIRT1 reverses the immune insensitivity in BAP1 deficient PDAC mouse model. CONCLUSION: Our study elucidates an unrevealed mechanism by which BAP1 regulates immune therapy response in PDAC via HSF1 inhibition, and providing promising therapeutic strategies to address immune insensitivity in BAP1-deficient PDAC.

Effects of Different Test Positions on Quantitative Muscle Strength of Wrist and Finger Flexor Muscle Groups and Its Standardization.

OBJECTIVES: To explore the effects of different test positions on quantitative muscle strength of wrist and finger flexor muscle groups and to establish a standardized muscle strength test protocol for each muscle group. METHODS: Forty healthy subjects (12 males and 28 females) were recruited. A portable digital quantitative muscle strength tester, Micro FET2TM, was used to measure the flexor muscle strength of each finger and the wrist joint at the 30° extension, 0° neutral, and 30° flexion, respectively. Palmar abduction strength of the thumb was measured at 30° and 60°, respectively. Ten subjects were randomly selected from the 40 subjects, and the quantitative muscle strength of each muscle group was tested again by the same operator after an interval of 10 to 15 days. RESULTS: Except for the fact that in males, there was no significant difference in flexor muscle strength of thumb and wrist joint between 30° of wrist extension and neutral 0° position, the muscle strength of the other fingers flexion and wrist palmar flexor showed the following characteristics：30° of wrist extension > neutral 0° position > 30° of flexion, and the PAST was 30°>60°; The flexor muscle strength of all the subjects was thumb > index finger > middle finger > ring finger > little finger; All muscle strength values of male were greater than those of female, and the difference was statistically significant (P<0.05); There was no significant difference between the left and right side muscle strength values of all subjects (P>0.05). The reliability of muscle strength values measured at different times in 10 subjects was good. CONCLUSIONS: The quantitative muscle strength of each muscle group of the hand and wrist is affected by the test position, and a standardized and uniformed test position should be adopted in the actual identification. Micro FET2TM has good reliability for hand and wrist quantitative muscle strength testing. The 30° extension of the wrist can be used as the best standardized test position for the flexion muscle strength of each finger and wrist joint. The 30° position can be used as the best standardized test position for PAST.

Ubiquitin ligase TRIM15 promotes the progression of pancreatic cancer via the upregulation of the IGF2BP2-TLR4 axis.

BACKGROUND: The tripartite motif family, predominantly characterized by its E3 ubiquitin ligase activities, is involved in various cellular processes including signal transduction, apoptosis and autophagy, protein quality control, immune regulation, and carcinogenesis. Tripartite Motif Containing 15 (TRIM15) plays an important role in melanoma progression through extracellular signal-regulated kinase activation; however, data on its role in pancreatic tumors remain lacking. We previously demonstrated that TRIM15 targeted lipid synthesis and metabolism in pancreatic cancer; however, other specific regulatory mechanisms remain elusive. METHODS: We used transcriptomics and proteomics, conducted a series of phenotypic experiments, and used a mouse orthotopic transplantation model to study the specific mechanism of TRIM15 in pancreatic cancer in vitro and in vivo. RESULTS: TRIM15 overexpression promoted the progression of pancreatic cancer by upregulating the toll-like receptor 4. The TRIM15 binding protein, IGF2BP2, could combine with TLR4 to inhibit its mRNA degradation. Furthermore, the ubiquitin level of IGF2BP2 was positively correlated with TRIM15. CONCLUSIONS: TRIM15 could ubiquitinate IGF2BP2 to enhance the function of phase separation and the maintenance of mRNA stability of TLR4. TRIM15 is a potential therapeutic target against pancreatic cancer.

Characterization of Pancreatic Fistula after Post-pancreatectomy Acute Pancreatitis.

OBJECTIVE: This study aimed to investigate the clinical significance and risk factors of postoperative pancreatic fistula (POPF) after post-pancreatectomy acute pancreatitis (PPAP) in patients who underwent pancreaticoduodenectomy (PD). SUMMARY BACKGROUND DATA: PPAP has been recognized as a critical factor in the pathophysiology of POPF after PD. METHODS: A total of 817 consecutive patients who underwent elective PD between January 2020 and June 2022 were included. PPAP and POPF were defined in accordance with the International Study Group for Pancreatic Surgery (ISGPS) definitions. Multivariate logistic analyses were performed to investigate the risk factors for POPF. Comparisons between PPAP-associated POPF and non-PPAP-associated POPF were made to further characterize this intriguing complication. RESULTS: Overall, 159 (19.5%) patients developed POPF after PD, of which 73 (45.9%) occurred following PPAP, and the remaining 86 (54.1%) had non-PPAP-associated POPF. Patients with PPAP-associated POPF experienced significantly higher morbidity than patients without POPF. Multivariate analyses revealed distinct risk factors for each POPF type. For PPAP-associated POPF, independent risk factors included estimated blood loss >200 mL (OR 1.93), MPD ≤3 cm (OR 2.88), and soft pancreatic texture (OR 2.01), largely overlapping with FRS (Fistula Risk Score) elements. On the other hand, non-PPAP-associated POPF was associated with age >65 years (OR 1.95), male (OR 2.10), and MPD ≤3 cm (OR 2.57). Notably, among patients with PPAP, the incidence of POPF consistently hovered around 50% regardless of the FRS stratification. CONCLUSIONS: PPAP-associated POPF presents as a distinct pathophysiology in the development of POPF after PD, potentially opening doors for future prevention strategies targeting the early postoperative period.

Geometric deep learning for the prediction of magnesium-binding sites in RNA structures.

Magnesium ions (Mg2+) are essential for the folding, functional expression, and structural stability of RNA molecules. However, predicting Mg2+-binding sites in RNA molecules based solely on RNA structures is still challenging. The molecular surface, characterized by a continuous shape with geometric and chemical properties, is important for RNA modelling and carries essential information for understanding the interactions between RNAs and Mg2+ ions. Here, we propose an approach named RNA-magnesium ion surface interaction fingerprinting (RMSIF), a geometric deep learning-based conceptual framework to predict magnesium ion binding sites in RNA structures. To evaluate the performance of RMSIF, we systematically enumerated decoy Mg2+ ions across a full-space grid within the range of 2 to 10 Å from the RNA molecule and made predictions accordingly. Visualization techniques were used to validate the prediction results and calculate success rates. Comparative assessments against state-of-the-art methods like MetalionRNA, MgNet, and Metal3DRNA revealed that RMSIF achieved superior success rates and accuracy in predicting Mg2+-binding sites. Additionally, in terms of the spatial distribution of Mg2+ ions within the RNA structures, a majority were situated in the deep grooves, while a minority occupied the shallow grooves. Collectively, the conceptual framework developed in this study holds promise for advancing insights into drug design, RNA co-transcriptional folding, and structure prediction.

The Value of VR-PVEP in Objective Evaluation of Monocular Refractive Visual Impairment.

OBJECTIVES: To study the virtual reality-pattern visual evoked potential (VR-PVEP) P100 waveform characteristics of monocular visual impairment with different impaired degrees under simultaneous binocular perception and monocular stimulations. METHODS: A total of 55 young volunteers with normal vision (using decimal recording method, far vision ≥0.8 and near vision ≥0.5) were selected to simulate three groups of monocular refractive visual impairment by interpolation method. The sum of near and far vision ≤0.2 was Group A, the severe visual impairment group; the sum of near and far vision <0.8 was Group B, the moderate visual impairment group; and the sum of near and far vision ≥0.8 was Group C, the mild visual impairment group. The volunteers' binocular normal visions were set as the control group. The VR-PVEP P100 peak times measured by simultaneous binocular perception and monocular stimulation were compared at four spatial frequencies 16×16, 24×24, 32×32 and 64×64. RESULTS: In Group A, the differences between P100 peak times of simulant visual impairment eyes and simultaneous binocular perception at 24×24, 32×32 and 64×64 spatial frequencies were statistically significant (P<0.05); and the P100 peak time of normal vision eyes at 64×64 spatial frequency was significantly different from the simulant visual impairment eyes (P<0.05). In Group B, the differences between P100 peak times of simulant visual impairment eyes and simultaneous binocular perception at 16×16, 24×24 and 64×64 spatial frequencies were statistically significant (P<0.05); and the P100 peak time of normal vision eyes at 64×64 spatial frequency was significantly different from the simulant visual impairment eyes (P<0.05). In Group C, there was no significant difference between P100 peak times of simulant visual impairment eyes and simultaneous binocular perception at all spatial frequencies (P>0.05). There was no significant difference in the P100 peak times measured at all spatial frequencies between simulant visual impairment eyes and simultaneous binocular perception in the control group (P>0.05). CONCLUSIONS: VR-PVEP can be used for visual acuity evaluation of patients with severe and moderate monocular visual impairment, which can reflect the visual impairment degree caused by ametropia. VR-PVEP has application value in the objective evaluation of visual function and forensic clinical identification.

Predicting visual acuity with machine learning in treated ocular trauma patients.

AIM: To predict best-corrected visual acuity (BCVA) by machine learning in patients with ocular trauma who were treated for at least 6mo. METHODS: The internal dataset consisted of 850 patients with 1589 eyes and an average age of 44.29y. The initial visual acuity was 0.99 logMAR. The test dataset consisted of 60 patients with 100 eyes collected while the model was optimized. Four different machine-learning algorithms (Extreme Gradient Boosting, support vector regression, Bayesian ridge, and random forest regressor) were used to predict BCVA, and four algorithms (Extreme Gradient Boosting, support vector machine, logistic regression, and random forest classifier) were used to classify BCVA in patients with ocular trauma after treatment for 6mo or longer. Clinical features were obtained from outpatient records, and ocular parameters were extracted from optical coherence tomography images and fundus photographs. These features were put into different machine-learning models, and the obtained predicted values were compared with the actual BCVA values. The best-performing model and the best variable selected were further evaluated in the test dataset. RESULTS: There was a significant correlation between the predicted and actual values [all Pearson correlation coefficient (PCC)>0.6]. Considering only the data from the traumatic group (group A) into account, the lowest mean absolute error (MAE) and root mean square error (RMSE) were 0.30 and 0.40 logMAR, respectively. In the traumatic and healthy groups (group B), the lowest MAE and RMSE were 0.20 and 0.33 logMAR, respectively. The sensitivity was always higher than the specificity in group A, in contrast to the results in group B. The classification accuracy and precision were above 0.80 in both groups. The MAE, RMSE, and PCC of the test dataset were 0.20, 0.29, and 0.96, respectively. The sensitivity, precision, specificity, and accuracy of the test dataset were 0.83, 0.92, 0.95, and 0.90, respectively. CONCLUSION: Predicting BCVA using machine-learning models in patients with treated ocular trauma is accurate and helpful in the identification of visual dysfunction.

A deep learning-based automatic tool for measuring the lengths of linear scars: forensic applications.

It is important to measure scars in forensic and clinical medicine. In practice, scars are mostly manually measured, and the results are diverse and influenced by various subjective factors. With the development of digital image technology and artificial intelligence, noncontact and automatic photogrammetry has been gradually used in some practical applications. In this article, we propose an automatic method for measuring the length of linear scars based on multiview stereo and deep learning, which combines the 3D reconstruction algorithm of structure from motion and the image segmentation algorithm based on a convolutional neural network. With a few pictures taken by a smart phone, automatic segmentation and measurement of scars can be realized. The reliability of the measurement was first demonstrated through simulation experiments on five artificial scars, giving errors of length <5%. Then, experiment results on 30 clinical scar samples showed that our measurements were in high agreement with manual measurements, with an average error of 3.69%. Our study demonstrates that the application of photogrammetry in scar measurement is effective and that the deep learning technique can realize the automation of scar measurement with high accuracy.

Correlation between Elbow Flexor Muscle Strength and Needle Electromyography Parameters after Musculocutaneous Nerve Injury.

OBJECTIVES: To explore the changes of elbow flexor muscle strength after musculocutaneous nerve injury and its correlation with needle electromyography (nEMG) parameters. METHODS: Thirty cases of elbow flexor weakness caused by unilateral brachial plexus injury (involving musculocutaneous nerve) were collected. The elbow flexor muscle strength was evaluated by manual muscle test (MMT) based on Lovett Scale. All subjects were divided into Group A (grade 1 and grade 2, 16 cases) and Group B (grade 3 and grade 4, 14 cases) according to their elbow flexor muscle strength of injured side. The biceps brachii of the injured side and the healthy side were examined by nEMG. The latency and amplitude of the compound muscle action potential (CMAP) were recorded. The type of recruitment response, the mean number of turns and the mean amplitude of recruitment potential were recorded when the subjects performed maximal voluntary contraction. The quantitative elbow flexor muscle strength was measured by portable microFET 2 Manual Muscle Tester. The percentage of residual elbow flexor muscle strength (the ratio of quantitative muscle strength of the injured side to the healthy side) was calculated. The differences of nEMG parameters, quantitative muscle strength and residual elbow flexor muscle strength between the two groups and between the injured side and the healthy side were compared. The correlation between elbow flexor manual muscle strength classification, quantitative muscle strength and nEMG parameters was analyzed. RESULTS: After musculocutaneous nerve injury, the percentage of residual elbow flexor muscle strength in Group B was 23.43% and that in Group A was 4.13%. Elbow flexor manual muscle strength classification was significantly correlated with the type of recruitment response, and the correlation coefficient was 0.886 (P<0.05). The quantitative elbow flexor muscle strength was correlated with the latency and amplitude of CMAP, the mean number of turns and the mean amplitude of recruitment potential, and the correlation coefficients were -0.528, 0.588, 0.465 and 0.426 (P<0.05), respectively. CONCLUSIONS: The percentage of residual elbow flexor muscle strength can be used as the basis of muscle strength classification, and the comprehensive application of nEMG parameters can be used to infer quantitative elbow flexor muscle strength.

Identifying TME signatures for cervical cancer prognosis based on GEO and TCGA databases.

The mortality rate from cervical cancer (CESC), a malignant tumor that affects women, has increased significantly globally in recent years. The discovery of biomarkers points to a direction for the diagnosis of cervical cancer with the advancement of bioinformatics technology. The goal of this study was to look for potential biomarkers for the diagnosis and prognosis of CESC using the GEO and TCGA databases. Because of the high dimension and small sample size of the omic data, or the use of biomarkers generated from a single omic data, the diagnosis of cervical cancer may be inaccurate and unreliable. The purpose of this study was to search the GEO and TCGA databases for potential biomarkers for the diagnosis and prognosis of CESC. We begin by downloading CESC (GSE30760) DNA methylation data from GEO, then perform differential analysis on the downloaded methylation data and screen out the differential genes. Then, using estimation algorithms, we score immune cells and stromal cells in the tumor microenvironment and perform survival analysis on the gene expression profile data and the most recent clinical data of CESC from TCGA. Then, using the 'limma' package and Venn plot in R language to perform differential analysis of genes and screen out overlapping genes, these overlapping genes were then subjected to GO and KEGG functional enrichment analysis. The differential genes screened by the GEO methylation data and the differential genes screened by the TCGA gene expression data were intersected to screen out the common differential genes. A protein-protein interaction (PPI) network of gene expression data was then created in order to discover important genes. The PPI network's key genes were crossed with previously identified common differential genes to further validate them. The Kaplan-Meier curve was then used to determine the prognostic importance of the key genes. Survival analysis has shown that CD3E and CD80 are important for the identification of cervical cancer and can be considered as potential biomarkers for cervical cancer.