OBJECTIVE: We investigated the postoperative renal function in persistent cloaca (PC) patients who underwent posterior sagittal anorecto-urethro-vaginopalsty (PSARUVP) and factors influencing the renal functional outcomes.
METHODS: A questionnaire survey was distributed to 244 university and children\'s hospitals across Japan. Of the 169 patients underwent PSARUVP, 103 patients were enrolled in the present study. Exclusion criteria was patients without data of renal prognosis.
RESULTS: The present study showed that renal anomalies (p = 0.09), vesicoureteral reflux (VUR) (p = 0.01), and hydrocolpos (p = 0.07) were potential factors influencing a decline in the renal function. Approximately half of the patients had a normal kidney function, but 45.6% had a reduced renal function (Stage ≥ 2 chronic kidney disease: CKD). The incidence of VUR was significantly higher in the renal function decline (RFD) group than those in the preservation (RFP) group (p = 0.01). Vesicostomy was significantly more frequent in the RFD group than in the RFP group (p = 0.04). Urinary tract infections (p < 0.01) and bladder dysfunction (p = 0.04) were significantly more common in patients with VUR than in patients without VUR. There was no association between the VUR status and the bowel function.
CONCLUSIONS: Prompt assessment and treatment of VUR along with bladder management may minimize the decline in the renal function.

A patient was delivered at 26 weeks (about 6 months) gestation via an emergency caesarian section. A patent ductus arteriosus (PDA) and atrial septal defect (ASD) were discovered during an echocardiogram 13 days after birth. The patient had catheter-based closure of the PDA and ASD. During a routine echocardiogram to check device placements, it was discovered that there was dilation of the superior vena cava (SVC), and it was suspected that a thrombus was present. Computed tomography angiography (CTA) was completed to better define SVC anatomy and flow acceleration. The CTA demonstrated that there was a double innominate vein.

OBJECTIVE: To examine the clinical characteristics and auditory performance of patients with CHARGE syndrome following cochlear implantation (CI), as well as the prognostic factors affecting auditory outcomes.
METHODS: Retrospective cohort.
METHODS: Tertiary academic center.
METHODS: A retrospective chart review was performed in patients with CHARGE syndrome who underwent CI from 2007 to 2022. The category of auditory performance (CAP) score was used to assess the CI outcomes, and factors that may affect the speech outcomes were also evaluated.
RESULTS: In 14 children with CHARGE syndrome, 22 CIs were performed, 6 unilaterally and 8 bilaterally. The mean age at CI was 25.9 months (range: 10-62). All patients had ear abnormalities and developmental delays, and cochlear nerve deficiency (CND) was present in all ears. At the last follow-up (mean: 49.6 months), the mean CAP score improved significantly compared to the preoperative measure (from 0.36 ± 0.81 to 3.21 ± 1.70, P = .001), with 6 patients (42.9%) achieving a CAP score of 4 points or higher. However, between the unilateral and bilateral CI groups, the final CAP score or change in CAP score was similar. Factors including age, coloboma, and CND did not significantly affect speech outcomes (all P > .05).
CONCLUSIONS: Even though CHARGE syndrome features challenging anomalies, CI can be conducted safely and can offer effective contribution to significant speech improvement. Patients with CHARGE syndrome should be given the opportunity to undergo CI to maximize their audiological progress.

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The purpose of this research was to use a historical method and core principles from scientific philosophy to explain why mistakes were made in the development of the lactic acidosis construct. On a broader scope, this research explains what science is, why some scientists despite good intention, often get it wrong, and why it takes so long (decades) to correct these errors. Science is a human behaviour that consists of the identification of a problem based on the correct application of prior knowledge, the development of a method to best resolve or test the problem, completion of these methods to acquire results, and then a correct interpretation of the results. If these steps are done correctly there is an increased probability (no guarantee) that the outcome is likely to be correct. Thomas Kuhn proposed that you can understand what science is from how it has been performed, and from his essays he revealed a very dysfunctional form of science that he called \'normal\' (due the preponderance of its presence) science. Conversely, Karl Popper was adamant that the practice of \'normal\' science revealed numerous flaws that deviate from fundamental principles that makes science, science. Collectively, the evidence reveals that within the sports medicine and health sciences, as with all disciplines, errors in science are more frequent than you might expect. There is an urgent need to improve how we educate and train scientists to prevent the pursuit of \'normal\' science and the harm it imparts on humanity.

The literature on cross-sectional stock return predictability has documented over 450 factors. We take the perspective of an institutional investor and navigate this zoo of factors by focusing on the evidence relevant to the practicalities of factor-based investment strategies. Establishing a sound theoretical rationale is key to identifying \"true\" factors, and we emphasize the need to recognize data-mining concerns that may cast doubt on the relevance of many factors. From a practical investment perspective, much of the factor evidence documented by academics may be more apparent than real. The performance of many factors is dependent on the inclusion of small- and micro-cap stocks in academic studies, although such stocks would likely be excluded from the real investment universe due to illiquidity and transaction costs. Nevertheless, a parsimonious set of factors emerges in equities and other asset classes, including currencies, fixed income, and commodities. These factors can serve as meaningful ingredients to factor-based portfolio construction.

With its exponential growth, the Internet of Things (IoT) has produced unprecedented levels of connectivity and data. Anomaly detection is a security feature that identifies instances in which system behavior deviates from the expected norm, facilitating the prompt identification and resolution of anomalies. When AI and the IoT are combined, anomaly detection becomes more effective, enhancing the reliability, efficacy, and integrity of IoT systems. AI-based anomaly detection systems are capable of identifying a wide range of threats in IoT environments, including brute force, buffer overflow, injection, replay attacks, DDoS assault, SQL injection, and back-door exploits. Intelligent Intrusion Detection Systems (IDSs) are imperative in IoT devices, which help detect anomalies or intrusions in a network, as the IoT is increasingly employed in several industries but possesses a large attack surface which presents more entry points for attackers. This study reviews the literature on anomaly detection in IoT infrastructure using machine learning and deep learning. This paper discusses the challenges in detecting intrusions and anomalies in IoT systems, highlighting the increasing number of attacks. It reviews recent work on machine learning and deep-learning anomaly detection schemes for IoT networks, summarizing the available literature. From this survey, it is concluded that further development of current systems is needed by using varied datasets, real-time testing, and making the systems scalable.

Methane-oxidizing bacteria (MOB) have long been considered as a microbial indicator for oil and gas prospecting. However, due to the phylogenetically narrow breath of ecophysiologically distinct MOB, classic culture-dependent approaches could not discriminate MOB population at fine resolution, and accurately reflect the abundance of active MOB in the soil above oil and gas reservoirs. Here, we presented a novel microbial anomaly detection (MAD) strategy to quantitatively identify specific indicator methylotrophs in the surface soils for bioprospecting oil and gas reservoirs by using a combination of 13C-DNA stable isotope probing (SIP), high-throughput sequencing (HTS), quantitative PCR (qPCR) and geostatistical analysis. The Chunguang oilfield of the Junggar Basin was selected as a model system in western China, and type I methanotrophic Methylobacter was most active in the topsoil above the productive oil wells, while type II methanotrophic Methylosinus predominated in the dry well soils, exhibiting clear differences between non- and oil reservoir soils. Similar results were observed by quantification of Methylobacter pmoA genes as a specific bioindicator for the prediction of unknown reservoirs by grid sampling. A microbial anomaly distribution map based on geostatistical analysis further showed that the anomalous zones were highly consistent with petroleum, geological and seismic data, and validated by subsequent drilling. Over seven years, a total of 24 wells have been designed and drilled into the targeted anomaly, and the success rate via the MAD prospecting strategy was 83 %. Our results suggested that molecular techniques are powerful tools for oil and gas prospecting. This study indicates that the exploration efficiency could be significantly improved by integrating multi-disciplinary information in geophysics and geomicrobiology while reducing the drilling risk to a greater extent.

UNASSIGNED: We encountered a colon cancer case with a very rare anomaly of the middle colic artery (MCA) originating from the splenic artery (SA).
UNASSIGNED: A woman was referred to our hospital for transverse colon cancer. Three-dimensional computed tomography (3D-CT) angiography showed an anomalous MCA originating from the SA rather than from the superior mesenteric artery (SMA) as is typical. Laparoscopic left hemicolectomy with D3 lymph node dissection was performed. The lymph nodes around the SMA were dissected from the caudal view, confirming the absence of a typical MCA. An anomalous SA-originating MCA was identified just below the pancreas, where it was clipped and ligated; subsequently, total mesenteric excision was achieved.
UNASSIGNED: As D3 lymph node dissection for transverse colon cancer is technically difficult, 3D-CT angiography is useful for identifying vascular anomalies preoperatively, thereby avoiding intraoperative injury. This is the first case report of laparoscopic colectomy associated with a SA-originating MCA anomaly.

Since 2015, there has been an increase in articles on anomaly detection in robotic systems, reflecting its growing importance in improving the robustness and reliability of the increasingly utilized autonomous robots. This review paper investigates the literature on the detection of anomalies in Autonomous Robotic Missions (ARMs). It reveals different perspectives on anomaly and juxtaposition to fault detection. To reach a consensus, we infer a unified understanding of anomalies that encapsulate their various characteristics observed in ARMs and propose a classification of anomalies in terms of spatial, temporal, and spatiotemporal elements based on their fundamental features. Further, the paper discusses the implications of the proposed unified understanding and classification in ARMs and provides future directions. We envisage a study surrounding the specific use of the term anomaly, and methods for their detection could contribute to and accelerate the research and development of a universal anomaly detection system for ARMs.