Search

Background: The coronavirus disease 2019 (COVID-19) pandemic has affected cancer care. The aim of this study was to clarify the trend of colorectal cancer (CRC) stage distribution in Japan during the COVID-19 pandemic. Methods: In this retrospective study, we used an inpatient medical claims database established at approximately 400 acute care hospitals. From the database, we searched patients who were identified as having the main disease (using International Classification of Diseases, 10th revision codes [C18.0–C20]) between January 2018 and December 2020. A multivariate logistic regression analysis was used to determine the impact of the pandemic on CRC stage distribution each month, and the odds ratio (OR) for late-stage cancer was calculated. Results: We analyzed 99,992 CRC patients. Logistic regression analysis, including the interaction term between increased late-stage CRC effect during the pandemic period and by each individual month, showed that the OR for late-stage CRC was highest in July during the pandemic, at 1.31 (95% confidence interval [CI], 1.13–1.52) and also significantly higher in September at 1.16 (95% CI, 1.00–1.35). Conclusion: We investigated the trend of CRC stage distribution during the COVID-19 pandemic using a nationwide hospital-claims database in Japan and found that the proportion of early-stage cancers tended to decrease temporarily after the state of emergency declaration due to the COVID-19 pandemic, but the effect was only temporary.

Abstract Background Coronary artery bypass grafting (CABG) using the right gastroepiploic artery (RGEA) is a well-established, safe procedure. However, problems with RGEA grafts in subsequent abdominal surgeries can lead to fatal complications. This report presents the first case of right hepatectomy for hepatocellular carcinoma after CABG using the RGEA. Case presentation We describe a case in which a right hepatectomy for an 81-year-old male patient with hepatocellular carcinoma was safely performed after CABG using a RGEA graft. Preoperatively, three-dimensional computed tomography (3D- CT) images were constructed to confirm the run of the RGEA graft. The operation was conducted with the standby of a cardiovascular surgeon if there was a problem with the RGEA graft. The RGEA graft had formed adhesions with the hepatic falciform ligament, necessitating meticulous dissection. After the right hepatectomy, the left hepatic lobe descended into the vacated space, exerting traction on the RGEA. However, this traction was mitigated by suturing the hepatic falciform ligament to the abdominal wall, ensuring stability of the RGEA. There were no intraoperative or postoperative complications. Conclusion It is crucial to confirm the functionality and anatomy of the RGEA graft preoperatively, handle it gently intraoperatively, and collaborate with cardiovascular surgeons.

Gastrointestinal stromal tumor (GIST) is the most prevalent mesenchymal tumor of the digestive tract. Its growth is primarily influenced by mutations in KIT or PDGFRA. Surgery is the primary treatment option for GIST; however, KIT inhibitors, such as imatinib, are used for inoperable cases. Resistance to imatinib is an upcoming challenge, especially because the effectiveness of alternative drugs is limited. Enhancement of the glycolysis pathway in cancer cells has been identified as a key feature in cancer. This unique metabolic activity has implications on tumor growth, prognosis, and resistance to therapy, even in GIST. Members of the glucose transporter (GLUT) family (particularly GLUT-1) play a significant role in GIST progression and response to treatment. Diagnostic imaging using 18F-fluorodeoxyglucose positron emission tomography/computed tomography, which enables visualization of glucose metabolism, can aid in GIST diagnosis and risk assessment. The interplay between glycolysis and GIST can lead to the development of various therapeutic strategies, especially those involving glycolysis-related molecules, such as hexokinase and lactate dehydrogenase. However, further research is required to understand the full spectrum of glycolysis in GIST and its therapeutic potential. Herein, we present an exhaustive overview and analysis of the role of glycolysis in GIST, especially as a therapeutic target.

Background: The oncological relevance of proximal gastrectomy in advanced gastric cancer remains unclear. We aimed to examine the frequency of lymph node metastasis in advanced gastric cancer to determine the oncological validity of proximal gastrectomy selection. Materials and methods: This study included consecutive 71 patients with locally advanced gastric cancer in the upper third of the stomach who underwent total gastrectomy at our institution between 2001 and 2017. Lymph node metastasis and its therapeutic value index were examined to identify candidates for proximal gastrectomy. Metastatic and 3-year overall survival rates of numbers 3a and 3b lymph nodes were examined from 2010 to 2019. Results: The metastatic rate and therapeutic value index of numbers 4d, 5, 6, and 12a lymph nodes were zero or low. The number 3 lymph node had a metastatic rate and therapeutic value index of 36.6 % and 31.1, respectively. The metastatic and 3-year overall survival rates of the number 3a lymph node were 32.7 % and 89 %, respectively, whereas those of the number 3b lymph node were 3.8 % and 100 %, respectively. All patients with positive metastasis to the number 3b lymph node received adjuvant chemotherapy. Histopathological findings of positive metastasis to the number 3b lymph node were located in the lesser curvature, and the tumor diameter exceeded 40 mm. Conclusion: For advanced gastric cancer of the upper third of the stomach, the indications of localization to the lesser curvature and a tumor diameter of >40 mm should be considered cautiously.

Abstract Polyps are well-known cancer precursors identified by colonoscopy. However, variability in their size, appearance, and location makes the detection of polyps challenging. Moreover, colonoscopy surveillance and removal of polyps are highly operator-dependent procedures and occur in a highly complex organ topology. There exists a high missed detection rate and incomplete removal of colonic polyps. To assist in clinical procedures and reduce missed rates, automated methods for detecting and segmenting polyps using machine learning have been achieved in past years. However, the major drawback in most of these methods is their ability to generalise to out-of-sample unseen datasets from different centres, populations, modalities, and acquisition systems. To test this hypothesis rigorously, we, together with expert gastroenterologists, curated a multi-centre and multi-population dataset acquired from six different colonoscopy systems and challenged the computational expert teams to develop robust automated detection and segmentation methods in a crowd-sourcing Endoscopic computer vision challenge. This work put forward rigorous generalisability tests and assesses the usability of devised deep learning methods in dynamic and actual clinical colonoscopy procedures. We analyse the results of four top performing teams for the detection task and five top performing teams for the segmentation task. Our analyses demonstrate that the top-ranking teams concentrated mainly on accuracy over the real-time performance required for clinical applicability. We further dissect the devised methods and provide an experiment-based hypothesis that reveals the need for improved generalisability to tackle diversity present in multi-centre datasets and routine clinical procedures.

The remarkable increase in published medical imaging datasets for chest X-rays has significantly improved the performance of deep learning techniques to classify lung diseases efficiently. However, large datasets require special arrangements to make them suitable, accessible, and practically usable in remote clinics and emergency rooms. Additionally, it increases the computational time and image-processing complexity. This study investigates the efficiency of converting the 2D chest X-ray into one-dimensional texture representation data using descriptive statistics and local binary patterns, enabling the use of feed-forward neural networks to efficiently classify lung diseases within a short time and with cost effectiveness. This method bridges diagnostic gaps in healthcare services and improves patient outcomes in remote hospitals and emergency rooms. It also could reinforce the crucial role of technology in advancing healthcare. Utilizing the Guangzhou and PA datasets, our one-dimensional texture representation achieved 99% accuracy with a training time of 10.85 s and 0.19 s for testing. In the PA dataset, it achieved 96% accuracy with a training time of 38.14 s and a testing time of 0.17 s, outperforming EfficientNet, EfficientNet-V2-Small, and MobileNet-V3-Small. Therefore, this study suggests that the dimensional texture representation is fast and effective for lung disease classification.

Nowadays, the use of drones as a fundamental element of smart cities has attracted the attention of many researchers to monitor and control the traffic of vehicles. Because of the high flexibility of multi-drone systems, like flying ad hoc networks (FANETs), they provide various services and improve modern life in smart cities. However, due to the unique features of FANET, especially the high speed of drones and rapid changes in network topology, communication reliability is a serious challenge in this network. Hence, traditional routing protocols, such as optimized link state routing (OLSR) scheme, cannot work well in these networks. In this paper, a smart filtering-based adaptive optimized link state routing (SFA-OLSR) scheme is proposed in FANETs. To increase adaptability to the FANET environment, SFA-OLSR provides a new solution to adjust the hello broadcast period so that each flying node specifies its broadcast period based on a new scale called cosine similarity between real and predicted positions. Furthermore, in SFA-OLSR, each flying node develops a filtering algorithm based on two parameters, namely link lifetime and remaining energy. The purpose of this algorithm is to reduce the size of the single-hop neighboring set of each flying node and minimize the search space when finding multi-point relays (MPRs). This increases the convergence speed of the algorithm. Then, SFA-OLSR exploits the sparrow search algorithm (SSA) to single out the best MPRs. This algorithm introduces a multi-objective function by focusing on three components, including energy, link lifespan, and neighbor degree. Lastly, the simulation process of SFA-OLSR is performed by the NS3 simulator. This process evaluates the performance of the proposed method and three schemes, namely Gangopadhyay et al., P-OLSR, and OLSR-ETX. These evaluations show that SFA-OLSR has a good performance in terms of three scales, namely packet delivery ratio, delay, and throughput, but its overhead is more than other methods.

Abstract Physical Unclonable Functions (PUFs) are widely used in cryptographic authentication and key-agreement protocols due to their unique physical properties. This article presents a comprehensive cryptanalysis of two recently developed authentication protocols, namely PLAKE and EV-PUF, both relying on PUFs. Our analysis reveals significant vulnerabilities in these protocols, including susceptibility to impersonation and key leakage attacks, which pose serious threats to the security of the underlying systems. In the case of PLAKE, we propose an attack that can extract the shared secret key with negligible complexity by eavesdropping on consecutive protocol sessions. Similarly, we demonstrate an efficient attack against EV-PUF that enables the determination of the shared key between specific entities. Furthermore, we highlight the potential for a single compromised client in the EV-PUF protocol to compromise the security of the entire network, leaving it vulnerable to pandemic attacks. These findings underscore the critical importance of careful design and rigorous evaluation when developing PUF-based authentication protocols. To address the identified vulnerabilities, we present an improved PUF-based authentication protocol that ensures robust security against all the attacks described in the context of PLAKE and EV-PUF. Through this research, we contribute to the field by exposing vulnerabilities in existing PUF-based authentication protocols and offering an improved protocol that enhances security and safeguards against various attack vectors. This work serves as a valuable reference for researchers and practitioners involved in the design and implementation of secure authentication schemes for IoT systems and dynamic charging systems for electric vehicles.

Abstract Gastric cancer is one of the leading causes of death worldwide, and resections are performed to cure the disease. We have previously reported the changes in the gastric microbiota after gastric cancer resection, which may be associated with the oral microbiota; however, the changes in the oral microbiota remain uncharacterized. This study aimed to characterize the changes in the salivary microbiota caused by gastric cancer resection and to evaluate their association with the gastric fluid microbiota. Saliva and gastric fluid samples were collected from 63 patients who underwent gastrectomy before and after surgery, and a 16S rRNA metagenomic analysis was performed to compare the microbiota composition. The number of bacterial species in the salivary microbiota decreased, and the bacterial composition changed after the resection of gastric cancer. In addition, we identified several bacterial genera that varied significantly in the salivary microbiota, some of which also showed similar changes in the gastric fluid microbiota. These findings indicate that changes in the gastric environment affect the oral microbiota, emphasizing the close association between the oral and gastric fluid microbiota. Our study signifies the importance of focusing on the oral microbiota in the perioperative period of gastrectomy in patients with gastric cancer.

Skin cancer has a significant impact on the lives of many individuals annually and is recognized as the most prevalent type of cancer. In the United States, an estimated annual incidence of approximately 3.5 million people receiving a diagnosis of skin cancer underscores its widespread prevalence. Furthermore, the prognosis for individuals afflicted with advancing stages of skin cancer experiences a substantial decline in survival rates. This paper is dedicated to aiding healthcare experts in distinguishing between benign and malignant skin cancer cases by employing a range of machine learning and deep learning techniques and different feature extractors and feature selectors to enhance the evaluation metrics. In this paper, different transfer learning models are employed as feature extractors, and to enhance the evaluation metrics, a feature selection layer is designed, which includes diverse techniques such as Univariate, Mutual Information, ANOVA, PCA, XGB, Lasso, Random Forest, and Variance. Among transfer models, DenseNet-201 was selected as the primary feature extractor to identify features from data. Subsequently, the Lasso method was applied for feature selection, utilizing diverse machine learning approaches such as MLP, XGB, RF, and NB. To optimize accuracy and precision, ensemble methods were employed to identify and enhance the best-performing models. The study provides accuracy and sensitivity rates of 87.72% and 92.15%, respectively.

Flying ad hoc networks (FANETs) have particular importance in various military and civilian applications due to their specific features, including frequent topological changes, the movement of drones in a three-dimensional space, and their restricted energy. These features have created challenges for designing cluster-based routing protocols. In this paper, a Q-learning-based smart clustering routing method (QSCR) is suggested in FANETs. In QSCR, each node discovers its neighbors through the periodic exchange of hello messages. The hello time interval is different in each cluster, and cluster leaders determine this interval based on the average speed similarity. Next, an adaptive clustering process is presented for categorizing drones in the clusters. In this step, the cluster leader is selected based on a new parameter called merit value, which includes residual energy, centrality, neighbor degree, speed similarity, and link validity time. Then, a centralized Q-learning model is presented to tune weight coefficients related to merit parameters dynamically. In the last step, the routing process is done using a greedy forwarding technique. Finally, QSCR is run on NS2, and the simulation results of QSCR are compared with those of ICRA, WCA, and DCA. These results show that QSCR carries out the clustering process rapidly but has less cluster stability than ICRA. QSCR gets energy efficiency and improves network lifetime. In the routing process, QSCR has a high packet delivery rate compared to other schemes. Also, the number of isolated clusters created in QSCR is less than other clustering methods. However, the proposed scheme has a higher end-to-end delay than ICRA. Also, this scheme experiences more communication overhead than ICRA slightly.

Real-life time series datasets exhibit complications that hinder the study of time series forecasting (TSF). These datasets inherently exhibit non-stationarity as their distributions vary over time. Furthermore, the intricate inter- and intra-series relationships among data points pose challenges for modeling. Many existing TSF models overlook one or both of these issues, resulting in inaccurate forecasts. This study proposes a novel TSF model designed to address the challenges posed by real-life data, delivering accurate forecasts in both multivariate and univariate settings. First, we propose methods termed “weak-stationarizing” and “non-stationarity restoring” to mitigate distributional shift. These methods enable the removal and restoration of non-stationary components from individual data points as needed. Second, we utilize the spectral decomposition of weak-stationary time series to extract informative features for forecasting. To learn features from the spectral decomposition of weak-stationary time series, we exploit a mixer architecture to find inter- and intra-series dependencies from the unraveled representation of the overall time series. To ensure the efficacy of our model, we conduct comparative evaluations against state-of-the-art models using six real-world datasets spanning diverse fields. Across each dataset, our model consistently outperforms or yields comparable results to existing models.

Texture describes the unique features of an image. Therefore, texture classification is a crucial task in computer vision. Various CNN-based deep learning methods have been developed to classify textures. During training, the deep-learning model undergoes an end-to-end procedure of learning features from low to high levels. Most CNN architectures depend on high-level features for the final classification. Hence, other low- and mid-level information was not prioritized for the final classification. However, in the case of texture classification, it is essential to determine detailed feature information within the pattern to classify textures as they have diversity and irregularity in images within the same class. Therefore, the feature information at the low- and mid-levels can also provide meaningful information to distinguish the classes. In this study, we introduce a CNN model with a feature retention module (FRM) to utilize features from numerous levels. FRM maintains the texture information extracted at each level and extracts feature information through filters of various sizes. We used three texture datasets to evaluate the proposed model combined with the FRM. The experimental results showed that learning using different levels of features together assists in improving learning performance more than learning using high-level features.

Replenishing tumor-suppressor miRNAs (TS-miRNAs) is a potential next-generation nucleic acid–based therapeutic approach. Establishing an effective miRNA delivery system is essential to successful TS-miRNA therapy. To overcome vulnerability to RNA nucleases, we previously developed a chemically modified miRNA143-3p (CM-miR-143). In clinical practice, colorectal cancer (CRC) pelvic recurrence is an occasional challenge following curative resection, requiring a novel therapy because reoperative surgery poses a significant burden to the patient. Hence, we considered the use of CM-miR-143 as an alternative treatment. In this study, we used a mouse model bearing pelvic CRC adjacent to the rectum and investigated the anticancer effects of CM-miR-143 lipoplexes formulated from miRNA and a cationic liposome. Compared with commercial synthetic miR-143, CM-miR-143 lipoplexes accumulated heavily in regions of the pelvic CRC tumor where the blood flow was high. As a result, systemic administration of CM-miR-143 lipoplexes improved animal survival by significantly suppressing pelvic CRC tumors and relieving a lethal bowel obstruction caused by rectal compression. Detailed protein analysis revealed that the myristoylated alanine-rich C kinase is a novel target for CM-miR-143 lipoplexes. Our results suggest that CM-miR-143 is a potential next-generation drug candidate in the treatment of CRC pelvic recurrence.

Abstract Background Inguinal endometriosis is a rare clinical disease with an unclear etiology and pathogenesis, and its diagnosis requires accurate medical history-taking and histological examination. However, surgical treatment for the condition has not yet been standardized. This report presents two cases of inguinal endometriosis. Case presentation The first patient was a 36-year-old woman who complained of pain and swelling in her right inguinal region. Physical examination revealed a soft, tender right inguinal mass. The size of the mass repeatedly increased and decreased during menstruation and did not show swelling with abdominal pressure. Magnetic resonance imaging showed a 3.5 × 2.5 cm mass with high intensity on T2-weighted imaging in the right inguinal canal, and no communication was found between the lesion site and the abdominal cavity. We diagnosed this case as inguinal endometriosis and managed it using an anterior approach and laparoscopic observation. The second patient was a 51-year-old woman who presented with an intermittently painful mass in her right inguinal region. The mass tended to increase in size, with worsening pain before menstruation. Abdominal computed tomography revealed a 2 × 2 cm cystic mass in the right inguinal region. We made a diagnosis of inguinal ectopic endometriosis and decided to operate via the totally extraperitoneal (TEP) method for excision plus transabdominal observation. The postoperative course in both cases was uneventful with no recurrence. Conclusions Inguinal endometriosis is a rare entity that should be suspected in patients with cyclical symptoms of inguinal pain and swelling that correlate with their menstrual cycle, which might otherwise be attributed to inguinal hernia. It is crucial to make a preoperative diagnosis based on a careful medical review, physical examination, and imaging studies, and to make an appropriate surgical plan. Particularly, in the case of ectopic inguinal endometriosis involving the canal of Nuck, laparoscopic observation is useful for the intraoperative diagnosis of inguinal endometriosis to help rule out the involvement of other abdominal sites. However, it is important to select and modify the surgical technique to avoid rupturing the endometrisis mass and prevent postoperative recurrence.

Abstract Background Accessory breast carcinomas of the axilla of males are rare, and primary breast neuroendocrine tumors (BNETs) are rare as well. We present a case of a BNET arising in the axilla of a man. Case presentation A 64-year-old Japanese man presented with a hard 15-mm mass in the axilla and axillary lymph node swelling. Histopathological examination of the incisional biopsy specimen revealed a neuroendocrine carcinoma. Therefore, wide radical excision of the axillary tumor and axillary lymph node dissection were performed. Hematoxylin and eosin staining showed that the solid tumor was mainly located in the subcutaneous adipose tissues and appeared to invade the skin. The tumor phenotypes were positive for CAM 5.2, synaptophysin, estrogen receptor, progesterone receptor, and GATA-binding protein 3; they were negative for human epidermal growth receptor 2. The neuroendocrine component comprised more than 90% of the tumor, and the Ki-67 index was 21%. These results indicated that the tumor was a BNET. This patient underwent adjuvant chemotherapy, endocrine therapy, and radiotherapy. Conclusions BNET cases in males are rare. The clinical and histological criteria as well as treatment for these rare cases are discussed.

Abstract This prospective study aimed to evaluate presepsin use as a biomarker of on postoperative infectious complications after gastrectomy, compared to C-reactive protein (CRP), white blood cells (WBCs), and neutrophils (Neuts). Overall, 108 patients were enrolled between October 2019 and December 2020. Presepsin, CRP, WBC, and Neut levels were measured preoperatively and on postoperative days (PODs) 1, 3, 5, and 7, using a postoperative morbidity survey. Grade II or higher infectious complications occurred in 18 patients (16.6%). Presepsin levels on all evaluated PODs were significantly higher in the infectious complication group than in the non-complication group (p = 0.002, p 0.8 (0.86) on POD 5. For presepsin, the optimal cut-off values were 298 pg/mL (sensitivity, 83.3%; specificity, 83.3%), 278 pg/mL (sensitivity, 83.3%; specificity, 82.2%), and 300 pg/mL (sensitivity, 83.3%; specificity, 82%) on PODs 3, 5, and 7, respectively. Presepsin levels on PODs 3, 5, and 7 after gastrectomy is a more useful biomarker of postoperative infectious complications compared to CRP, WBCs, and Neuts, with a high sensitivity and specificity.

With the emergence of the Internet of Things (IoT), the computer networks’ phenomenal expansion, and enormous relevant applications, data is continuously increasing. In this way, cybersecurity has gained significant importance in protecting networks from different cyber-attacks like Intrusions, Denial-of-Service (DoS), Eavesdropping, Rushing Attack, etc. A traditional Intrusion Detection System (IDS) tangled with the clustering technique plays a vital role in modern security. Still, it has limitations to analyze the vast volumes of data to identify an anomaly intelligently. Machine learning is a technique that may be tangled with the MapReduce-Based Intelligent Model for Intrusion Detection (MR-IMID) to automate intrusion detection intelligently. MR-IMID is proposed to detect intrusions on a network with multiple data classification tasks in this research work. The proposed MR-IMID processes big data sets reliably using commodity hardware. In this proposed research work, multiple network sources are being utilized in Real-time for intrusion detection. In this proposed research, the MR-IMID detects intrusions by predicting unknown test scenarios and stores the data in the database to minimize future inconsistencies. The detection accuracy of the proposed model during training and validation phases is 97.7% and 95.7%, respectively, which is better than previously published approaches.

BackgroundBreast cancer (BC) is the most common cancer in women globally. The North Africa and Middle East (NAME) region is coping hard with the burden of BC. We aimed to present the latest epidemiology of BC and its risk factors in this region.MethodsWe retrieved the data on BC burden and risk factors from the Global Burden of Disease Study 2019 to describe BC status in the 21 countries of the NAME region from 1990 to 2019. We explored BC incidence, prevalence, deaths, disability-adjusted life years (DALYs), and attributable burden to seven risk factors of female BC, namely, alcohol use, diet high in red meat, low physical activity, smoking, secondhand smoke, high body mass index, and high fasting plasma glucose. Decomposition analysis on BC incidence trend was done to find out the contributing factors to this cancer’s growth.ResultsIn 2019, there were 835,576 (95% uncertainty interval: 741,968 to 944,851) female and 10,938 (9,030 to 13,256) male prevalent cases of BC in the NAME region. This number leads to 35,405 (30,676 to 40,571) deaths among female patients and 809 (654 to 1,002) deaths in male patients this year. BC was responsible for 1,222,835 (1,053,073 to 1,411,009) DALYs among female patients in 2019, with a greater proportion (94.9%) of burden in years of life lost (YLLs). The major contributor to female BC incidence increase in the past three decades was found to be increase in age-specific incidence rates of BC (227.5%), compared to population growth (73.8%) and aging (81.8%). The behavioral risk factors were responsible for majority of attributable female BC burden (DALYs: 106,026 [66,614 to 144,247]). High fasting plasma glucose was found to be the risk factor with the largest effect (DALYs: 84,912 [17,377 to 192,838]) on female BC burden.ConclusionThe increasing incidence and burden of BC in the NAME region is remarkable, especially when considering limited resources in the developing countries of this region. Proper policies like expanding screening programs and careful resource management are needed to effectively manage BC burden.

Summary: Background: South Korea has one of the longest operating universal health coverage (UHC) systems. A comprehensive analysis of long-term trajectories of morbidity and mortality in the South Korean population after the inception of UHC is needed to inform health-care policy and practice. Methods: We used data from the Global Burden of Disease Study (GBD) 2019 to present estimates of cause-specific mortality, incidence, prevalence, years of life lost (YLLs), years of life lived with disability, and disability-adjusted life-years (DALYs) in South Korea from 1990 to 2019. We also examined forecasted estimates of YLLs up to 2040 to investigate likely future changes in disease burden. Finally, we evaluated GBD estimates from seven comparator countries to place disease burden in South Korea within a broader context. Findings: Age-standardised DALYs related to non-communicable diseases (NCDs) decreased by 43·6% (95% uncertainty interval [UI] 39·4–47·9) and mortality by 58·8% (55·9–60·5) from 1990 to 2019. In 2019, the ratio of male to female age-standardised rates of YLLs in South Korea was higher than the global average for 75·9% (22 of 29 diseases) of leading causes, indicating a disproportional disease burden on males in South Korea. Among risk factors, tobacco use accounted for the highest number of 2019 deaths (44 470 [95% UI 37 432–53 989]) in males and high systolic blood pressure for the highest number (21 014 [15 553–26 723]) in females. Among the top ten leading causes of YLLs forecast in South Korea in 2040, nine were NCDs, for both males and females. Interpretation: Our report shows a positive landscape of population health outcomes in South Korea following the establishment of UHC. However, due in part to the effects of population ageing driving up medical expenditures for NCDs, financial pressures and sustainability challenges associated with UHC are pressing concerns. Policy makers should work to tackle population ageing and allocate resources efficiently by prioritising interventions that address the leading causes of death and disability identified in this study. Funding: Bill & Melinda Gates Foundation.

In polyp segmentation, the latest notable topic revolves around polyp generalization, which aims to develop deep learning-based models capable of learning from single or multiple source domains and applying this knowledge to unseen datasets. A significant challenge in real-world clinical settings is the suboptimal performance of generalized models due to domain shift. Convolutional neural networks (CNNs) are often biased towards low-level features, such as style features, impacting generalization. Despite attempts to mitigate this bias using data augmentation techniques, learning model-agnostic and class-specific feature representations remains complex. Previous methods have employed image-level transformations with styles to supplement training data diversity. However, these approaches face limitations in ensuring style diversity due to restricted style sources, limiting the utilization of the potential style space. To address this, we propose a straightforward yet effective style conversion and generation module integrated into the UNet model. This module transfers diverse yet plausible style features to the original training data at the feature-level space, ensuring that generated styles align closely with the original data. Our method demonstrates superior performance in single-domain generalization tasks across five datasets compared to prior methods.

Technological advancements have shifted human living and working environments from outdoor to indoor. Although indoor spaces offer protection from unfavorable weather conditions, they also present new health challenges. Stale, humid, and warm indoor air creates an ideal breeding ground for bacteria and fungi, leading to health issues such as asthma and bacterial infections. Although proper ventilation is crucial, a comprehensive inspection of local indoor air quality is necessary to prevent widespread diseases. In vitro experiments involving bacteria and fungi collected from indoor air yield accurate results but are time- and cost-intensive. In silico methods offer faster results and provide valuable insights for guiding further in vitro experiments. In this study, we conduct an in vitro cytotoxicity assay on 32 fungi species and compare its results with a memory-efficient in silico modeling method using parameter-efficient fine-tuning (PEFT) and ProtBERT. This study suggests a potential methodology for predicting the toxicity of indoor airborne fungi when their identities are known.

Abstract Polyp segmentation has accomplished massive triumph over the years in the field of supervised learning. However, obtaining a vast number of labeled datasets is commonly challenging in the medical domain. To solve this problem, we employ semi-supervised methods and suitably take advantage of unlabeled data to improve the performance of polyp image segmentation. First, we propose an encoder-decoder-based method well suited for the polyp with varying shape, size, and scales. Second, we utilize the teacher-student concept of training the model, where the teacher model is the student model’s exponential average. Third, to leverage the unlabeled dataset, we enforce a consistency technique and force the teacher model to generate a similar output on the different perturbed versions of the given input. Finally, we propose a method that upgrades the traditional pseudo-label method by learning the model with continuous update of pseudo-label. We show the efficacy of our proposed method on different polyp datasets, and hence attaining better results in semi-supervised settings. Extensive experiments demonstrate that our proposed method can propagate the unlabeled dataset’s essential information to improve performance.

We report a case of delayed perforation following esophageal endoscopic submucosal dissection (ESD). A patient with Parkinson’s disease presented with two superficial carcinomatous lesions in the middle third of the esophagus. ESD was performed, and 4/5 of the esophageal circumference was resected, including the adjacent lesion area. Immediately post-ESD, triamcinolone acetonide was injected into the submucosa underlying the ulcer to prevent scarring and stenosis. Histopathological examination of the resected specimen revealed squamous cell carcinoma limited to the lamina propria with negative margins. Seventeen days post-ESD, the patient experienced sudden-onset chest pain during a meal. Computed tomography showed pneumomediastinum, which indicated a delayed perforation. We immediately performed subtotal esophagectomy. A sharply torn longitudinal perforation was present in the post-ESD ulcer. Delayed perforation after esophageal ESD is extremely rare. In this case, the perforation might have been caused by food impaction and delayed ulcer healing due to triamcinolone injection.

Summary: Background: The objective of this study is to investigate the trends of exposure and burden attributable to the four main metabolic risk factors, including high systolic blood pressure (SBP), high fasting plasma glucose (FPG), high body-mass index (BMI), and high low-density lipoproteins cholesterol (LDL) in North Africa and the Middle East from 1990 to 2019. Methods: The data were retrieved from Global Burden of Disease Study 2019. Summary exposure value (SEV) was used for risk factor exposure. Burden attributable to each risk factor was incorporated in the population attributable fraction to estimate the total attributable deaths and disability-adjusted life-years (DALYs). Findings: While age-standardized death rate (ASDR) attributable to high-LDL and high-SBP decreased by 26.5% (18.6–35.2) and 23.4% (15.9–31.5) over 1990–2019, respectively, high-BMI with 5.1% (−9.0–25.9) and high-FPG with 21.4% (7.0–37.4) change, grew in ASDR. Moreover, age-standardized DALY rate attributed to high-LDL and high-SBP declined by 30.2% (20.9–39.0) and 25.2% (16.8–33.9), respectively. The attributable age-standardized DALY rate of high-BMI with 8.3% (−6.5–28.8) and high-FPG with 27.0% (14.3–40.8) increase, had a growing trend. Age-standardized SEVs of high-FPG, high-BMI, high-SBP, and high-LDL increased by 92.4% (82.8–103.3), 76.0% (58.9–99.3), 10.4% (3.8–18.0), and 5.5% (4.3–7.1), respectively. Interpretation: The burden attributed to high-SBP and high-LDL decreased during the 1990–2019 period in the region, while the attributable burden of high-FPG and high-BMI increased. Alarmingly, exposure to all four risk factors increased in the past three decades. There has been significant heterogeneity among the countries in the region regarding the trends of exposure and attributable burden. Urgent action is required at the individual, community, and national levels in terms of introducing effective strategies for prevention and treatment that account for local and socioeconomic factors. Funding: Bill & Melinda Gates Foundation.

Abstract A 40‐year‐old Japanese male presented with epigastric pain and loss of appetite at a general hospital three years ago. Computed tomography revealed massive thickening of the gastric wall, and gastroscopy revealed diffuse erythema and edematous thickening of the gastric mucosa. Thereafter, epigastric pain and gastric wall thickening recurred frequently, causing an inability to intake food. Conservative treatment was marginally effective; therefore, a distal gastrectomy was performed. Postoperatively, the patient resumed food intake without complications. Histopathological examination of the surgical specimen revealed Heinrich type 1 gastric ectopic pancreas (EP) with pancreatitis. In this case, the gastric wall's massive thickening was caused by gastric EP's pancreatitis. Although there are some reports of pancreatitis of gastric EP, there are no detailed reports of endoscopic findings, including endoscopic ultrasonography and the disease progression. Recurrent pancreatitis of EP leads to forming a septum within the gastric wall, resulting in a hematoma. Eventually, irreversible narrowing of the gastric lumen may occur, as observed in the present case. We consider this an important case report presenting detailed pathogenesis supported by endoscopic and pathohistological findings of surgical specimens. Our study will help in the early diagnosis and better management of the condition.

Abstract The changes in gastric microbiota following reconstruction after gastrectomy have not been reported. This study aimed to compare the gastric microbiota following Billroth I and Roux-en-Y reconstructions after distal gastrectomy. We enrolled 71 gastrectomized patients with gastric cancer; 31 and 40 underwent Billroth I and Roux-en-Y reconstructions, respectively. During upper gastrointestinal endoscopy, gastric fluid was collected immediately before and 6 months after distal gastrectomy. Deoxyribonucleic acid isolated from each sample was evaluated using 16S ribosomal ribonucleic acid metagenomic analysis. Analysis revealed that the gastric microbiota’s species richness (expressed as the alpha diversity) was significantly lower after than before distal gastrectomy (operational taxonomic units, p = 0.001; Shannon index, p = 0.03). The interindividual diversity (beta diversity) was significantly different before and after distal gastrectomy (unweighted UniFrac distances, p = 0.04; weighted UniFrac distances, p = 0.001; Bray–Curtis, p = 0.001). Alpha and beta diversity were not significantly different between Billroth I and Roux-en-Y reconstructions (observed operational taxonomic units, p = 0.58; Shannon index, p = 0.95; unweighted UniFrac distances, p = 0.65; weighted UniFrac distances, p = 0.67; Bray–Curtis, p = 0.63). Our study demonstrated significant differences in gastric microbiota diversity, composition, and community before and after distal gastrectomy but no difference between Billroth I and Roux-en-Y reconstruction after distal gastrectomy.

Summary: Background: The COVID-19 pandemic highlighted gaps in health surveillance systems, disease prevention, and treatment globally. Among the many factors that might have led to these gaps is the issue of the financing of national health systems, especially in low-income and middle-income countries (LMICs), as well as a robust global system for pandemic preparedness. We aimed to provide a comparative assessment of global health spending at the onset of the pandemic; characterise the amount of development assistance for pandemic preparedness and response disbursed in the first 2 years of the COVID-19 pandemic; and examine expectations for future health spending and put into context the expected need for investment in pandemic preparedness. Methods: In this analysis of global health spending between 1990 and 2021, and prediction from 2021 to 2026, we estimated four sources of health spending: development assistance for health (DAH), government spending, out-of-pocket spending, and prepaid private spending across 204 countries and territories. We used the Organisation for Economic Co-operation and Development (OECD)'s Creditor Reporting System (CRS) and the WHO Global Health Expenditure Database (GHED) to estimate spending. We estimated development assistance for general health, COVID-19 response, and pandemic preparedness and response using a keyword search. Health spending estimates were combined with estimates of resources needed for pandemic prevention and preparedness to analyse future health spending patterns, relative to need. Findings: In 2019, at the onset of the COVID-19 pandemic, US$9·2 trillion (95% uncertainty interval [UI] 9·1–9·3) was spent on health worldwide. We found great disparities in the amount of resources devoted to health, with high-income countries spending $7·3 trillion (95% UI 7·2–7·4) in 2019; 293·7 times the $24·8 billion (95% UI 24·3–25·3) spent by low-income countries in 2019. That same year, $43·1 billion in development assistance was provided to maintain or improve health. The pandemic led to an unprecedented increase in development assistance targeted towards health; in 2020 and 2021, $1·8 billion in DAH contributions was provided towards pandemic preparedness in LMICs, and $37·8 billion was provided for the health-related COVID-19 response. Although the support for pandemic preparedness is 12·2% of the recommended target by the High-Level Independent Panel (HLIP), the support provided for the health-related COVID-19 response is 252·2% of the recommended target. Additionally, projected spending estimates suggest that between 2022 and 2026, governments in 17 (95% UI 11–21) of the 137 LMICs will observe an increase in national government health spending equivalent to an addition of 1% of GDP, as recommended by the HLIP. Interpretation: There was an unprecedented scale-up in DAH in 2020 and 2021. We have a unique opportunity at this time to sustain funding for crucial global health functions, including pandemic preparedness. However, historical patterns of underfunding of pandemic preparedness suggest that deliberate effort must be made to ensure funding is maintained. Funding: Bill & Melinda Gates Foundation.

We hypothesized that an individual's physique is related to reconstruction length, as organs reconstructed via the retrosternal route are curved toward the ventral side. This study aimed to determine factors contributing to the reconstruction length of the retrosternal route. Fifty patients underwent subtotal esophagectomy with esophagogastric reconstruction via the posterior mediastinal route between 2010 and 2014; the esophagus-stomach and posterior mediastinal route lengths were measured to evaluate whether they could be approximated. Forty patients underwent reconstruction via the retrosternal route between 2015 and 2020; the esophagus-stomach and retrosternal route lengths were compared, and contributing factors were analyzed. Each length was measured perioperatively using three-dimensional enhanced computed tomography images. The associated factors obtained included age, sex, height, body weight, body mass index, thickness and height of the thorax, depth of the thoracic inlet space, thoracic curve, left hepatic lobe volume, and the thickness and height of the liver. The length of the esophagus-stomach could approximate that of the posterior mediastinal route [posterior mediastinal-esophagus-stomach; 0.04 (-0.5-0.6) cm, p = 0.77]. Using three-dimensional enhanced computed tomography images, the lengths of the esophagus-stomach and retrosternal routes were comparable, despite variability [retrosternal-esophagus-stomach; 0.72 (-0.1-1.8) cm, p = 0.095]. Analyzing factors associated with the length revealed a positive correlation of body weight, body mass index, and thickness of the thorax with the difference. A higher body mass index (OR = 1.7, 95% CI 1.1-2.8, p = 0.007) was associated with a longer retrosternal route in the multivariate analysis. An individual's physique is associated with the reconstruction length; particularly, the length of the retrosternal route was longer in patients with a high body mass index.

Traditional encoder–decoder networks like U-Net have been extensively used for polyp segmentation. However, such networks have demonstrated limitations in explicitly modeling long-range dependencies. In such networks, local patterns are emphasized over the global context, as each convolutional kernel focuses on only a local subset of pixels in the entire image. Several recent transformer-based networks have been shown to overcome such limitations. Such networks encode long-range dependencies using self-attention methods and thus learn highly expressive representations. However, due to the computational complexity of modeling the whole image, self-attention is expensive to compute, as there is a quadratic increment in cost with the increase in pixels in the image. Thus, patch embedding has been utilized, which groups small regions of the image into single input features. Nevertheless, these transformers still lack inductive bias, even with the image as a 1D sequence of visual tokens. This results in the inability to generalize to local contexts due to limited low-level features. We introduce a hybrid transformer combined with a convolutional mixing network to overcome computational and long-range dependency issues. A pretrained transformer network is introduced as a feature-extracting encoder, and a mixing module network (MMNet) is introduced to capture the long-range dependencies with a reduced computational cost. Precisely, in the mixing module network, we use depth-wise and 1 × 1 convolution to model long-range dependencies to establish spatial and cross-channel correlation, respectively. The proposed approach is evaluated qualitatively and quantitatively on five challenging polyp datasets across six metrics. Our MMNet outperforms the previous best polyp segmentation methods.

Abstract Background The incidence of remnant gastric cancer (RGC) after distal gastrectomy is 1–5%. However, as the survival rate of patients with gastric cancer improves due to early detection and treatment, more patients may develop RGC. There is no consensus on the surgical and postoperative management of RGC, and the clinicopathological characteristics correlated with the long-term outcomes remain unclear. Therefore, we investigated the clinicopathological factors associated with the long-term outcomes of RGC. Methods We included 65 consecutive patients who underwent gastrectomy for RGC from January 2000 to December 2015 at the Osaka Medical and Pharmaceutical University Hospital, Japan. The Kaplan–Meier method was used to create survival curves, and differences in survival were compared between the groups (clinical factors, pathological factors, and surgical factors) using the log-rank test. Multivariate analyses using the Cox proportional hazard model were used to identify factors associated with long-term survival. Results No significant differences were noted in the survival rate based on clinical factors (age, body mass index, diabetes mellitus, hypertension, cardiovascular disease, pulmonary complications, liver disease, diet, history of alcohol drinking, and history of smoking) or the type of remnant gastrectomy. Significant differences were noted in the survival rate based on pathological factors and surgical characteristics (intraoperative blood loss, operation time, and the number of positive lymph nodes). Multivariate analysis revealed that the T stage (hazard ratio, 5.593; 95% confidence interval [CI], 1.183–26.452; p = 0.030) and venous invasion (hazard ratio, 3.351; 95% CI, 1.030–10.903; p = 0.045) were significant independent risk factors for long-term survival in patients who underwent radical resection for RGC. Conclusions T stage and venous invasion are important prognostic factors of long-term survival after remnant gastrectomy for RGC and may be keys to managing and identifying therapeutic strategies for improving prognosis in RGC.