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Remote sensing is an effective method of evaluating building damage after a large-scale natural disaster, such as an earthquake or a typhoon. In recent years, with the development of computer vision technology, deep learning algorithms have been used for damage assessment from aerial images. In April 2016, a series of earthquakes hit the Kyushu region, Japan, and caused severe damage in the Kumamoto and Oita Prefectures. Numerous buildings collapsed because of the strong and continuous shaking. In this study, a deep learning model called Mask R-CNN was modified to extract residential buildings and estimate their damage levels from post-event aerial images. Our Mask R-CNN model employs an improved feature pyramid network and online hard example mining. Furthermore, a non-maximum suppression algorithm across multiple classes was also applied to improve prediction. The aerial images captured on 29 April 2016 (two weeks after the main shock) in Mashiki Town, Kumamoto Prefecture, were used as the training and test sets. Compared with the field survey results, our model achieved approximately 95% accuracy for building extraction and over 92% accuracy for the detection of severely damaged buildings. The overall classification accuracy for the four damage classes was approximately 88%, demonstrating acceptable performance.

Background miR-141 is up-regulated and plays crucial roles in nasopharyngeal carcinoma (NPC). However, the molecular mechanism underlying the dysregulation of miR-141 is still obscure. Methods Thus, the ChIP-PCR was performed to identify the c-Myc-binding sites in miR-141 and BRD7. qRT-PCR, western blot and immunohistochemistry assays were used to detect the expression of miR-141 and its up/down stream molecules. The rescue experiments on the c-Myc/miR-141 axis were performed in vitro and in vivo. Results Our results showed that the levels of mature miR-141, pre-miR-141 and pri-miR-141 were downregulated in c-Myc knockdown NPC cells. Meanwhile, c-Myc transactivates the expression of miR-141 by binding its promoter region. Moreover, BRD7 was identified as a co-factor of c-Myc to negatively regulate the activation of c-Myc/miR-141 axis, as well as a direct target of c-Myc. Moreover, restoration of miR-141 in c-Myc knockdown NPC cells notably rescued the effect of c-Myc on cell proliferation and tumor growth, as well as the blocking of PTEN/AKT pathway. Additionally, the expression of c-Myc was positively correlated with that of miR-141 and the clinical stages of NPC patients and negatively associated with the expression of BRD7. Our findings demonstrated that BRD7 expression and c-Myc activation forms a negative feedback loop to control the cell proliferation and tumor growth by targeting miR-141. Conclusions These observations provide new mechanistic insights into the dysregulation of miR-141 expression and a promising therapeutic option for NPC.

Some studies have reported better or equivalent survival when treatment access was more equitable [4, 5–6], though treatment intensity, sequencing, and timing from diagnosis were not strictly matched between racial groups, leaving the possibility of unmeasured biological or molecular risk. Maintenance therapy differed, with BA more frequently receiving an immunomodulatory drug [IMiD] and proteasome inhibitor [PI] (S1), reflecting evolving treatment practices and the increasing representation of older and non-White patients at our institution in more recent eras. Because the BA cohort was relatively small compared with WA and imbalanced across key demographic and treatment variables, we emphasized rigorous matching to reduce bias and address limitations of the small BA sample size. Factors All, % (n/N) BA, % (n/N) WA, % (n/N) OR (95% CI) p Demographics  Age ≤ 50 31% (157/507) 30% (39/131) 31% (118/376) 0.93 (0.60–1.43) 0.815  51 < Age < 65 54% (276/507) 56% (73/131) 54% (203/376) 1.07 (0.72–1.60) 0.809  Age ≥ 65 15% (74/507) 15% (19/131) 15% (55/376) 0.99 (0.56–1.74) 1.000  Female 52% (266/507) 55% (72/131) 52% (194/376) 1.14 (0.77–1.71) 0.574 Myeloma Risk Factors  Calcium >10.5 mmol/L 10% (50/502) 9% (12/128) 10% (38/374) 0.91 (0.46–1.81) 0.932  LDH high 20% (102/501) 26% (33/128) 18% (69/373) 1.53 (0.95–2.46) 0.101  Creatine ≥ 2 mg/L 9% (43/503) 7% (9/129) 9% (34/374) 0.75 (0.35–1.61) 0.577  CRP ≥ 8 mg/L 32% (158/497) 33% (41/126) 32% (117/371) 1.05 (0.68–1.61) 0.922  Albumin < 3.5 g/dL 36% (181/502) 38% (49/128) 35% (132/374) 1.14 (0.75–1.72) 0.616  B2M ≥ 3.5 mg/dL 47% (235/500) 41% (52/127) 49% (183/373) 0.72 (0.48–1.08) 0.139  B2M > 5.5 mg/dL 23% (114/500) 24% (30/127) 23% (84/373) 1.06 (0.66–1.71) 0.894  HGB – low g/dL 42% (215/506) 50% (66/131) 40% (149/375) 1.54 (1.03–2.30) 0.043  Platelets < 150 (K/uL) 14% (73/506) 18% (24/131) 13% (49/375) 1.49 (0.87–2.55) 0.184  PC % > 60% 34% (164/484) 35% (43/122) 33% (121/362) 1.08 (0.70–1.67) 0.797  > 7 MRI lesions 54% (272/502) 53% (69/131) 55% (203/371) 0.92 (0.62–1.37) 0.763  > 3 PET lesions 47% (213/458) 45% (54/119) 47% (159/339) 0.94 (0.62–1.43) 0.857 Light Chain  Urine M ≥ 500 mg/24 hr 32% (151/479) 32% (40/125) 31% (111/354) 1.03 (0.66–1.60) 0.983  κ 64% (319/502) 65% (84/130) 63% (235/372) 1.06 (0.70–1.61) 0.850  κ Ulight 60% (123/206) 56% (28/50) 61% (95/156) 0.82 (0.43–1.56) 0.654  Log(κ) > median 33% (144/439) 33% (36/110) 33% (108/329) 1.00 (0.63–1.58) 1.000  Log(λ) > median 48% (211/439) 45% (50/110) 49% (161/329) 0.87 (0.56–1.34) 0.601 Iron Metabolism  Iron – low (ug/dL) 5% (22/425) 6% (6/109) 5% (16/316) 1.09 (0.42–2.87) 1.000  Transferrin < 200 mg/dL 44% (109/246) 48% (33/69) 43% (76/177) 1.22 (0.70–2.13) 0.582  Ferritin–high (ng/mL) 32% (134/418) 38% (42/110) 30% (92/308) 1.45 (0.92–2.29) 0.138  TIBC < 250 ug/dL 31% (130/422) 38% (41/108) 28% (89/314) 1.55 (0.98–2.45) 0.081 General  BMI > 30 31% (128/415) 45% (43/96) 27% (85/319) 2.23 (1.39–3.58) 0.001  HDL – low (mg/dL) 68% (84/124) 70% (26/37) 67% (58/87) 1.18 (0.51–2.72) 0.855  BP Syst. > 140 mm Hg 30% (127/424) 35% (37/105) 28% (90/319) 1.38 (0.87–2.21) 0.215  BP Diast. > 90 mm Hg 10% (43/424) 15% (16/105) 8% (27/319) 1.94 (1.00–3.77) 0.071 Metaphase Cytogenetics  CA 32% (145/457) 33% (35/106) 31% (110/351) 1.08 (0.68–1.72) 0.836 FISH  1p del 19% (74/390) 21% (21/101) 18% (53/289) 1.17 (0.66–2.06) 0.694  1q gain 42% (170/407) 39% (41/105) 43% (129/302) 0.86 (0.55–1.35) 0.588  13q del 46% (165/357) 37% (37/99) 50% (128/258) 0.61 (0.38–0.97) 0.050  17p del 11% (38/352) 9% (9/95) 11% (29/257) 0.82 (0.37–1.81) 0.770 GEP Groups  t(11;14) 23% (118/507) 18% (23/131) 25% (95/376) 0.63 (0.38–1.05) 0.093  Hyperdiploid 43% (217/507) 45% (59/131) 42% (158/376) 1.13 (0.76–1.69) 0.618  t(4;14) 7% (37/507) 10% (13/131) 6% (24/376) 1.62 (0.80–3.27) 0.251  t(14;16)/t(14;20) 14% (70/507) 15% (19/131) 14% (51/376) 1.08 (0.61–1.91) 0.903  PR 13% (65/507) 13% (17/131) 13% (48/376) 1.02 (0.56–1.84) 1.000  SRCA 66% (335/507) 63% (82/131) 67% (253/376) 0.81 (0.54–1.23) 0.385  HRCA 34% (172/507) 37% (49/131) 33% (123/376) 1.23 (0.81–1.86) 0.385 Risk Scores  ISS stage I 45% (208/465) 50% (60/119) 43% (148/346) 1.36 (0.90–2.07) 0.180  ISS stage II 29% (136/465) 25% (30/119) 31% (106/346) 0.76 (0.48–1.22) 0.315  ISS stage III 26% (121/465) 24% (29/119) 27% (92/346) 0.89 (0.55–1.44) 0.723  GEP70 ≥ 0.66 16% (82/507) 17% (22/131) 16% (60/376) 1.06 (0.62–1.81) 0.931 Tandem Transplant  Tandem ASCT 83% (421/507) 84% (110/131) 83% (311/376) 1.09 (0.64–1.87) 0.845 Maintenance  No PI/IMiD 13% (66/507) 11% (15/131) 14% (51/376) 0.82 (0.45–1.52) 0.640  IMiD–Based 13% (67/507) 15% (19/131) 13% (48/376) 1.16 (0.65–2.06) 0.722  PI +IMiD 74% (374/507) 74% (97/131) 74% (277/376) 1.02 (0.65–1.60) 1.000 Analysis of clinical and genetic parameters for all (All), Black (BA), and White (WA) matched patients in UARK-NDMM (n = 507). PC %, plasma cell percentage; MRI, magnetic resonance imaging; PET, positron emission tomography; Urine M, urine M-protein; Iron–low indicates <35 µg/dL (female) or <50 µg/dL (male); ferritin–high indicates >306 ng/mL (female) or >336 ng/mL (male); TIBC, total iron-binding capacity; BMI, body mass index; HDL, high-density lipoprotein < 50 mg/dL (female) or < 40 mg/dL (male).

Richter syndrome (RS), characterized by aggressive lymphoma arising from chronic lymphocytic leukaemia (CLL), presents a poor response to treatment and grim prognosis. To elucidate RS mechanisms, paired samples from a patient with DLBCL-RS were subjected to single-cell RNA sequencing (scRNA-seq) and high-throughput chromosome conformation capture (Hi-C) sequencing. Over 10,000 cells were profiled via scRNA-seq, revealing the comprehensive B cell transformation in RS. Hi-C sequencing exposed a unique chromatin architecture in RS, with increased proximal and decreased distal interactions. At the compartment scale, the interaction between B compartments was strengthened in DLBCL cells, while topologically associating domains (TADs) in DLBCL had elevated intra-TAD and reduced inter-TAD contacts. Differentially expressed genes at TAD borders between CLL and DLBCL cells highlighted an enrichment of cAMP-mediated signalling. To substantiate the functional relevance of ATF1 and CAP1, the genes involve in cAMP-mediated signalling, in the context of cell proliferation, we have performed gain- and loss-of-function experiments in relevant cell lines. Collectively, integrated scRNA-seq and Hi-C data suggest that chromatin reorganization and altered cAMP signalling drive RS transformation.

Background LncRNA-PACERR plays critical role in the polarization of tissue-associated macrophages (TAMs). In this study, we found the function and molecular mechanism of PACERR in TAMs to regulate pancreatic ductal adenocarcinoma (PDAC) progression. Methods We used qPCR to analyse the expression of PACERR in TAMs and M1-tissue-resident macrophages (M1-NTRMs) which were isolated from 46 PDAC tissues. The function of PACERR on macrophages polarization and PDAC proliferation, migration and invasion were confirmed through in vivo and in vitro assays. The molecular mechanism of PACERR was discussed via fluorescence in situ hybridization (FISH), RNA pull-down, ChIP-qPCR, RIP-qPCR and luciferase assays. Results LncRNA-PACERR was high expression in TAMs and associated with poor prognosis in PDAC patients. Our finding validated that LncRNA-PACERR increased the number of M2-polarized cells and facilized cell proliferation, invasion and migration in vitro and in vivo. Mechanistically, LncRNA-PACERR activate KLF12/p-AKT/c-myc pathway by binding to miR-671-3p. And LncRNA-PACERR which bound to IGF2BP2 acts as an m6A-dependent manner to enhance the stability of KLF12 and c-myc in cytoplasm. In addition, the promoter of LncRNA-PACERR was a target of KLF12 and LncRNA-PACERR recruited EP300 to increase the acetylation of histone by interacting with KLF12 in nucleus. Conclusions This study found that LncRNA-PACERR functions as key regulator of TAMs in PDAC microenvironment and revealed the novel mechanisms in cytoplasm and in nucleus.

ObjectiveSurgical site infections (SSI) after hysterectomy constitute significant postoperative complications, affecting patient recovery and healthcare costs. We conducted a systematic review of risk factors for SSI in patients undergoing hysterectomy.DesignThe current study conducted a systematic review with meta-analysis to identify and summarise risk factors for SSI following hysterectomy.Data sourcesPubMed, Medline, Embase, Web of Science and Cochrane Central Register of Controlled Trials were searched through 1 November 2023.Eligibility criteriaThe inclusion criteria were (1) population: female participants who had post-hysterectomy SSI; (2) intervention: hysterectomy surgeries; (3) comparators: the number of participants who had or had not post-hysterectomy SSI; (4) outcomes: the number of participants exposed and not exposed to the risk factors of SSI. The exclusion criteria were (1) non-English studies and (2) studies that provided insufficient data.Data extraction and synthesisTwo reviewers conducted the screening process independently. Articles that did not meet the inclusion criteria were excluded. For those that met the criteria, full-text papers were procured. Any discrepancies between the reviewers were resolved through discussion. The meta-analysis synthesised risk factors reported in ≥4 datasets via random-effects models, assessing heterogeneity, sensitivity (leave-one-out), publication bias (Egger’s test/funnel plots) and subgroup analyses (incision types).ResultsBlood transfusion emerged as the largest risk factor (OR=2.55, 95% CI (1.84, 3.56)), followed by tumour presence (OR=2.23, 95% CI (1.86, 2.66)), obesity (OR=1.79, 95% CI (1.43, 2.23)), diabetes (OR=1.70, 95% CI (1.26, 2.29)) and tobacco use (OR=1.43, 95% CI (1.26, 1.63)). The ORs varied by incision type.ConclusionsThe study establishes blood transfusion, tumour presence, obesity, diabetes and tobacco use as significant risk factors for SSI after hysterectomy, with variations in risk evident across different incision types. The findings also suggest vaginal and laparoscopic hysterectomies as preferable alternatives to abdominal hysterectomy in mitigating SSI risk. Future research should aim for more granular data to untangle the interplay between comorbidities and further elucidate the differential risk across SSI types.

Objectives: This study aimed to explore the distribution and differences in the health status of migrant workers in China by gender and age. In addition, it investigated the causes of health inequalities among them. Methods: This paper analyzes the differences in health status across age groups for migrant workers of different genders based on the data from the China Migrant Dynamic Survey in 2018. It also empirically assesses how education level and health insurance impact gender-related health inequalities. Results: The results suggest that female migrant workers in China have significantly lower health levels than males. Furthermore, these differences in health are exacerbated with age. This disparity may be attributed to lower participation in social insurance participation and less educational attainment among female migrant workers than their male counterparts. Conclusion: The government should take effective practical measures to increase the social insurance participation rate of female migrant workers. Moreover, investing in female education to reduce health inequality among migrant workers is essential.

Although new inputs of acidic anions are decreasing, soil acidification still deserves more academic attention because of the effects of historical stores of SO42− already absorbed into soils. Forest canopy has large, species-specific effects on rainwater chemistry, for which the hydrological mechanism remains unclear. We investigated precipitation, throughfall, stemflow, and litter leachate across three forest types in a severely acid-polluted site located in Southwest China. Precipitation monitored over 4 months, representing summer, fall, winter, and spring, indicated neutral precipitation in Tieshanping with pH ranging from 6.58–7.33. Throughfall and litter leachate in Pinus massoniana Lamb. stands were enriched with greater cation and anion fluxes, as well as more dissolved organic carbon (DOC) flux. Rainwater from pure stands of Cinnamomum camphora (Linn) Presl yielded lower N and DOC inputs to soils with higher base saturation, which would reduce soil acidification and, therefore, improve the sustainability of forest ecosystems.