A Case Report on a Hybrid Approach to Managing Acute Large Bowel Obstruction Secondary to Spigelian Hernia.

Spigelian hernias are an uncommon type of primary ventral hernia and are defined as a defect in the Spigelian aponeurosis (fascia). Herein, we present an uncommon case of Spigelian hernia to highlight the potential complications of these hernias and the need for surgical management. This is a case report of an 86-year-old gentleman presenting post-fall with an acute rib fracture and an incidental Spigelian hernia seen on a CT trauma pan scan. The Spigelian hernia surgical treatment was planned for elective management due to the anesthetic risks associated with an elderly patient and acute rib fractures. Ultimately, the patient developed a large bowel obstruction secondary to the Spigelian hernia and required emergency operative management to relieve the obstruction. The patient had an uncomplicated recovery following his emergency surgery. This case report highlights the importance of assessing anesthetic risks versus surgical risks when it comes to surgical planning. Clinicians should recognize occult hernias and continue ongoing clinical reviews with a high index of suspicion, as symptoms of Spigelian hernia obstruction might be non-specific.

Risk factors and early markers for echovirus type 11 associated haemorrhage-hepatitis syndrome in neonates, a retrospective cohort study.

Background: Echovirus type 11(E-11) can cause fatal haemorrhage-hepatitis syndrome in neonates. This study aims to investigate clinical risk factors and early markers of E-11 associated neonatal haemorrhage-hepatitis syndrome. Methods: This is a multicentre retrospective cohort study of 105 neonates with E-11 infection in China. Patients with haemorrhage-hepatitis syndrome (the severe group) were compared with those with mild disease. Clinical risk factors and early markers of haemorrhage-hepatitis syndrome were analysed. In addition, cytokine analysis were performed in selective patients to explore the immune responses. Results: In addition to prematurity, low birth weight, premature rupture of fetal membrane, total parenteral nutrition (PN) (OR, 28.7; 95% CI, 2.8-295.1) and partial PN (OR, 12.9; 95% CI, 2.2-77.5) prior to the onset of disease were identified as risk factors of developing haemorrhage-hepatitis syndrome. Progressive decrease in haemoglobin levels (per 10 g/L; OR, 1.5; 95% CI, 1.1-2.0) and platelet (PLT) < 140 × 109/L at early stage of illness (OR, 17.7; 95% CI, 1.4-221.5) were associated with the development of haemorrhage-hepatitis syndrome. Immunological workup revealed significantly increased interferon-inducible protein-10(IP-10) (P < 0.0005) but decreased IFN-α (P < 0.05) in peripheral blood in severe patients compared with the mild cases. Conclusions: PN may potentiate the development of E-11 associated haemorrhage-hepatitis syndrome. Early onset of thrombocytopenia and decreased haemoglobin could be helpful in early identification of neonates with the disease. The low level of IFN-α and elevated expression of IP-10 may promote the progression of haemorrhage-hepatitis syndrome.

Protein Structure Prediction: Challenges, Advances, and the Shift of Research Paradigms.

Protein structure prediction is an interdisciplinary research topic that has attracted researchers from multiple fields, including biochemistry, medicine, physics, mathematics, and computer science. These researchers adopt various research paradigms to attack the same structure prediction problem: biochemists and physicists attempt to reveal the principles governing protein folding; mathematicians, especially statisticians, usually start from assuming a probability distribution of protein structures given a target sequence and then find the most likely structure, while computer scientists formulate protein structure prediction as an optimization problem - finding the structural conformation with the lowest energy or minimizing the difference between predicted structure and native structure. These research paradigms fall into the two statistical modeling cultures proposed by Leo Breiman, namely, data modeling and algorithmic modeling. Recently, we have also witnessed the great success of deep learning in protein structure prediction. In this review, we present a survey of the efforts for protein structure prediction. We compare the research paradigms adopted by researchers from different fields, with an emphasis on the shift of research paradigms in the era of deep learning. In short, the algorithmic modeling techniques, especially deep neural networks, have considerably improved the accuracy of protein structure prediction; however, theories interpreting the neural networks and knowledge on protein folding are still highly desired.

Accurate and efficient protein sequence design through learning concise local environment of residues.

MOTIVATION: Computational protein sequence design has been widely applied in rational protein engineering and increasing the design accuracy and efficiency is highly desired. RESULTS: Here, we present ProDESIGN-LE, an accurate and efficient approach to protein sequence design. ProDESIGN-LE adopts a concise but informative representation of the residue's local environment and trains a transformer to learn the correlation between local environment of residues and their amino acid types. For a target backbone structure, ProDESIGN-LE uses the transformer to assign an appropriate residue type for each position based on its local environment within this structure, eventually acquiring a designed sequence with all residues fitting well with their local environments. We applied ProDESIGN-LE to design sequences for 68 naturally occurring and 129 hallucinated proteins within 20 s per protein on average. The designed proteins have their predicted structures perfectly resembling the target structures with a state-of-the-art average TM-score exceeding 0.80. We further experimentally validated ProDESIGN-LE by designing five sequences for an enzyme, chloramphenicol O-acetyltransferase type III (CAT III), and recombinantly expressing the proteins in Escherichia coli. Of these proteins, three exhibited excellent solubility, and one yielded monomeric species with circular dichroism spectra consistent with the natural CAT III protein. AVAILABILITY AND IMPLEMENTATION: The source code of ProDESIGN-LE is available at https://github.com/bigict/ProDESIGN-LE.

The utility of ctDNA in colorectal cancer with peritoneal metastases.

BACKGROUND: The development of peritoneal metastases (PM) in patients with colorectal cancer (CRC) connotates a poor prognosis. Circulating tumour (ctDNA) is a promising tumour biomarker in the management CRC. This systematic review aimed to summarize the role of ctDNA in patients with CRC and PM. METHODS: Following the Preferred Reporting Items for Systematic Reviews and Meta-analyses (PRISMA) guidelines, a systematic review of the literature until June 2022 was performed. Studies reporting on the utility of ctDNA in colorectal PM were included. A total of eight eligible studies were identified including a total of 167 patients. RESULTS: The findings from this review suggest an evolving role for ctDNA in CRC with PM. ctDNA can be isolated from both plasma and peritoneal fluid, with peritoneal fluid preferred as the liquid biopsy of choice with higher mutation detection rates. Concordance rates between tissue and plasma/peritoneal ctDNA mutation detection can vary, but is generally high. ctDNA has a potential role in monitoring anti-EGFR treatment response and resistance, as well as in predicting future prognosis and recurrence. The detection of ctDNA in plasma of patients with isolated PM is also possibly suggestive of occult systemic disease, and patients exhibiting such ctDNA positivity may benefit from systemic treatment. Limitations to ctDNA mutation detection may include the size of peritoneal lesions, as well as the fact that PM poorly shed ctDNA. CONCLUSION: While these findings are promising, further large-scale studies are needed to better evaluate the utility of ctDNA in this subset of patients.

ProALIGN: Directly Learning Alignments for Protein Structure Prediction via Exploiting Context-Specific Alignment Motifs.

Template-based modeling (TBM), including homology modeling and protein threading, is one of the most reliable techniques for protein structure prediction. It predicts protein structure by building an alignment between the query sequence under prediction and the templates with solved structures. However, it is still very challenging to build the optimal sequence-template alignment, especially when only distantly related templates are available. Here we report a novel deep learning approach ProALIGN that can predict much more accurate sequence-template alignment. Like protein sequences consisting of sequence motifs, protein alignments are also composed of frequently occurring alignment motifs with characteristic patterns. Alignment motifs are context-specific as their characteristic patterns are tightly related to sequence contexts of the aligned regions. Inspired by this observation, we represent a protein alignment as a binary matrix (in which 1 denotes an aligned residue pair) and then use a deep convolutional neural network to predict the optimal alignment from the query protein and its template. The trained neural network implicitly but effectively encodes an alignment scoring function, which reduces inaccuracies in the handcrafted scoring functions widely used by the current threading approaches. For a query protein and a template, we apply the neural network to directly infer likelihoods of all possible residue pairs in their entirety, which could effectively consider the correlations among multiple residues. We further construct the alignment with maximum likelihood, and finally build a structure model according to the alignment. Tested on three independent data sets with a total of 6688 protein alignment targets and 80 CASP13 TBM targets, our method achieved much better alignments and 3D structure models than the existing methods, including HHpred, CNFpred, CEthreader, and DeepThreader. These results clearly demonstrate the effectiveness of exploiting the context-specific alignment motifs by deep learning for protein threading.

Seq-SetNet: directly exploiting multiple sequence alignment for protein secondary structure prediction.

MOTIVATION: Accurate prediction of protein structure relies heavily on exploiting multiple sequence alignment (MSA) for residue mutations and correlations as this information specifies protein tertiary structure. The widely used prediction approaches usually transform MSA into inter-mediate models, say position-specific scoring matrix or profile hidden Markov model. These inter-mediate models, however, cannot fully represent residue mutations and correlations carried by MSA; hence, an effective way to directly exploit MSAs is highly desirable. RESULTS: Here, we report a novel sequence set network (called Seq-SetNet) to directly and effectively exploit MSA for protein structure prediction. Seq-SetNet uses an 'encoding and aggregation' strategy that consists of two key elements: (i) an encoding module that takes a component homologue in MSA as input, and encodes residue mutations and correlations into context-specific features for each residue; and (ii) an aggregation module to aggregate the features extracted from all component homologues, which are further transformed into structural properties for residues of the query protein. As Seq-SetNet encodes each homologue protein individually, it could consider both insertions and deletions, as well as long-distance correlations among residues, thus representing more information than the inter-mediate models. Moreover, the encoding module automatically learns effective features and thus avoids manual feature engineering. Using symmetric aggregation functions, Seq-SetNet processes the homologue proteins as a sequence set, making its prediction results invariable to the order of these proteins. On popular benchmark sets, we demonstrated the successful application of Seq-SetNet to predict secondary structure and torsion angles of residues with improved accuracy and efficiency. AVAILABILITY AND IMPLEMENTATION: The code and datasets are available through https://github.com/fusong-ju/Seq-SetNet. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Anastomotic Leak in Colorectal Surgery: Predictive Factors and Survival.

<br><b>Introduction:</b> Anastomotic leak (AL) is a serious complication following colorectal surgery.</br> <br><b>Aim:</b> The aim of this study was to identify factors associated with the development of AL and to analyze its impact on survival.</br> <br><b>Materials and methods:</b> All consecutive adult colorectal cancer resections performed between 2007 and 2020 with curative intent and anastomosis formation were included from a prospectively maintained database. The primary outcome measure was the rate of AL. The secondary outcome measure was 5-year overall survival (OS).</br> <br><b>Results:</b> There were 6837 eligible patients. The rate of AL was 2.2% and 4.0% in patients with colon and rectal cancer, respectively. AL was a significant independent predictor of reduced 5-year OS in patients who underwent curative surgery for rectal cancer (odds ratio 2.293, p = 0.009). Emergency surgery (p = 0.015), surgery at a public hospital (p = 0.002), and an open surgical approach (p = 0.021) were all associated with a significantly higher risk of AL in patients with colon cancer, with higher rates of AL noted in left colectomies as compared to right hemicolectomies (4.4% <i>vs.</i> 1.3%, p < 0.001). In rectal cancer patients, AL was associated with neoadjuvant chemoradiotherapy (p = 0.038) and male gender (p = 0.002). The anastomosis formation technique (hand-sewn <i>vs.</i> stapled) did not impact the rate of AL (p = 0.116 and p = 0.198 with colon and rectal cancer, respectively).</br> <br><b>Discussion:</b> Clinicians should be cognizant of the predictive factors for AL and should consider early intervention for at-risk patients.</br>.

Predictive Factors for Anastomotic Leak in Colorectal Surgery: A Bi-National Database Study.

IntroductionAnastomotic leak (AL) is a serious complication following colorectal surgery. This study aimed to identify factors associated with the development of AL and analyze its impact on survival.Materials and MethodsAll consecutive adult colorectal cancer resections with curative intent and anastomosis formation were included from a prospectively maintained bi-national database between 2007 and 2020. The primary outcome measure was the rate of AL. The secondary outcome measure was 5-year overall survival (OS).ResultsThere were 7566 eligible patients. The rate of AL was 2.3% and 4.4% in patients with colon and rectal cancer respectively. AL was a significant independent predictor of reduced 5-year OS in patients who underwent curative surgery for rectal cancer (Odds ratio 1.999, p = 0.017). Emergency surgery (p = 0.013), surgery at a public hospital (p < 0.01), and an open surgical approach (p = 0.002) were all significantly associated with a higher risk of AL in patients with colon cancer, with higher rates of AL noted in left colectomies as compared to right hemicolectomies (6.8% vs 1.6%, p < 0.05). In rectal cancer patients, ultra-low anterior resections had the highest risk of AL (4.6%), and associations were found with neoadjuvant chemotherapy (p = 0.011), surgery in a public hospital (p = 0.019), and an open approach (p = 0.035). Anastomosis formation technique (hand-sewn vs stapled) did not impact on rate of AL.DiscussionClinicians should be cognizant of the predictive factors for AL and consider early intervention for patients at risk of this.

Organoids as a Robust Preclinical Model for Precision Medicine in Colorectal Cancer: A Systematic Review.

BACKGROUND: Patients with locally advanced or metastatic colorectal cancer (CRC) display heterogeneous responses to standard-of-care therapy. Robust preclinical models of malignancy in the form of patient-derived tumor organoids (PDTOs) have recently come to the fore in tailoring patient care to a personalized medicine level. This study aimed to review the literature systematically regarding PTDOs and gauge their impact on precision medicine in the management of CRC. METHODS: A PRISMA-compliant systematic review of the MEDLINE, EMBASE, Web of Science, and Cochrane Library databases was performed. The results were categorized based on the primary objective of the individual studies as follows: organoid use in predicting effective hyperthermic intraperitoneal chemotherapy (HIPEC), systemic chemotherapy in CRC, or neoadjuvant chemoradiotherapy in rectal cancer. RESULTS: The literature search found 200 publications, 16 of which met the inclusion criteria. Organoid models of primary and metastatic CRC have been increasingly used to assess clinical responses to standard therapy. Marked heterogeneity exists, matching the responses observed in clinical practice with ex vivo drug and radiation screening. Repeated correlation between organoid and patient sensitivity to forms of HIPEC, systemic chemotherapy, and chemoradiotherapy has been observed. CONCLUSION: Patient-derived tumor organoids are the latest tool in predictive translational research. Current organoid-based studies in precision medicine have shown their great potential for predicting the clinical response of patients to CRC therapy. Larger-scale, prospective data are required to fully support this exciting avenue in cancer care.

Long-term Patterns of Failure and the Value of Blood Prognostic Markers in Anal Cancers Treated With Intensity-Modulated Radiation Therapy.

BACKGROUND: To analyze the long-term outcomes and prognostic value of hematological parameters in anal cancer patients receiving intensity-modulated radiation therapy (IMRT). MATERIALS: Hospital records of consecutive patients with anal squamous cell carcinoma who received curative-intent IMRT according to a standardized contouring protocol between 2010 and 2020 were reviewed. Locoregional failure-free survival (LRFS), distant metastasis-free survival (DMFS), progression-free survival (PFS), and overall survival (OS) were estimated using the Kaplan-Meier method. Coverage of locoregional recurrences by the initial IMRT volumes were assessed. The prognostic value of pretreatment blood counts for PFS and OS were determined using Cox regression analysis. RESULTS: A total of 166 patients were analyzed with a median follow-up of 3.3 years. Forty-six percent and 54% of patients had Stage I-II and IIIA-B cancers, respectively. The 5-year LRFS, DMFS, PFS and OS were 81%, 89%, 65% and 76% respectively. Grade ≥ 3 toxicity occurred in 5% of patients. Of all patients who relapsed, 70% had only locoregional recurrence as first site of failure. Ninety percent of locoregional recurrences were in-field. Hemoglobin, neutrophil and platelet counts were associated with PFS on univariable analysis, but only cancer stage and p16 status remained prognostic on multivariable analysis. Patients with more advanced cancer stages also had higher baseline neutrophil counts. Performance status and neutrophil counts were prognostic for OS on multivariable analysis. CONCLUSION: This study affirms the long-term efficacy and safety of IMRT. Treatment resistance, rather than radiation geographic miss, is a major issue underpinning locoregional recurrences. Pretreatment blood counts were not validated to be independently prognostic for disease recurrence.

CopulaNet: Learning residue co-evolution directly from multiple sequence alignment for protein structure prediction.

Residue co-evolution has become the primary principle for estimating inter-residue distances of a protein, which are crucially important for predicting protein structure. Most existing approaches adopt an indirect strategy, i.e., inferring residue co-evolution based on some hand-crafted features, say, a covariance matrix, calculated from multiple sequence alignment (MSA) of target protein. This indirect strategy, however, cannot fully exploit the information carried by MSA. Here, we report an end-to-end deep neural network, CopulaNet, to estimate residue co-evolution directly from MSA. The key elements of CopulaNet include: (i) an encoder to model context-specific mutation for each residue; (ii) an aggregator to model residue co-evolution, and thereafter estimate inter-residue distances. Using CASP13 (the 13th Critical Assessment of Protein Structure Prediction) target proteins as representatives, we demonstrate that CopulaNet can predict protein structure with improved accuracy and efficiency. This study represents a step toward improved end-to-end prediction of inter-residue distances and protein tertiary structures.

ISSEC: inferring contacts among protein secondary structure elements using deep object detection.

BACKGROUND: The formation of contacts among protein secondary structure elements (SSEs) is an important step in protein folding as it determines topology of protein tertiary structure; hence, inferring inter-SSE contacts is crucial to protein structure prediction. One of the existing strategies infers inter-SSE contacts directly from the predicted possibilities of inter-residue contacts without any preprocessing, and thus suffers from the excessive noises existing in the predicted inter-residue contacts. Another strategy defines SSEs based on protein secondary structure prediction first, and then judges whether each candidate SSE pair could form contact or not. However, it is difficult to accurately determine boundary of SSEs due to the errors in secondary structure prediction. The incorrectly-deduced SSEs definitely hinder subsequent prediction of the contacts among them. RESULTS: We here report an accurate approach to infer the inter-SSE contacts (thus called as ISSEC) using the deep object detection technique. The design of ISSEC is based on the observation that, in the inter-residue contact map, the contacting SSEs usually form rectangle regions with characteristic patterns. Therefore, ISSEC infers inter-SSE contacts through detecting such rectangle regions. Unlike the existing approach directly using the predicted probabilities of inter-residue contact, ISSEC applies the deep convolution technique to extract high-level features from the inter-residue contacts. More importantly, ISSEC does not rely on the pre-defined SSEs. Instead, ISSEC enumerates multiple candidate rectangle regions in the predicted inter-residue contact map, and for each region, ISSEC calculates a confidence score to measure whether it has characteristic patterns or not. ISSEC employs greedy strategy to select non-overlapping regions with high confidence score, and finally infers inter-SSE contacts according to these regions. CONCLUSIONS: Comprehensive experimental results suggested that ISSEC outperformed the state-of-the-art approaches in predicting inter-SSE contacts. We further demonstrated the successful applications of ISSEC to improve prediction of both inter-residue contacts and tertiary structure as well.

Future directions in advanced penile cancer - mechanisms of carcinogenesis and a search for targeted therapy.

Penile squamous cell carcinoma (SCC) is a rare and aggressive urological malignancy. Advanced penile SCC requires multimodal management, including surgery and systemic therapy. Given its rarity, there have been few substantial advances in our understanding of the molecular and genomic drivers of penile SCC, especially for patients with relapsed or advanced disease. In this review, we discuss the molecular and genomic landscape of penile SCC, clinical trials in progress and implications for novel therapeutic targets. Future work should focus on preclinical models to provide a platform for investigation and validation of new molecular pathways for testing of therapeutics.

A randomized controlled trial protocol comparing the feeds of fresh versus frozen mother's own milk for preterm infants in the NICU.

BACKGROUND: Necrotizing enterocolitis (NEC) is the leading cause of death among preterm infants born at < 30 weeks' gestation. The incidence of NEC is reduced when infants are fed human milk. However, in many neonatal intensive care units (NICUs), it is standard practice to freeze and/or pasteurize human milk, which deactivates bioactive components that may offer additional protective benefits. Indeed, our pilot study showed that one feed of fresh mother's own milk per day was safe, feasible, and can reduce morbidity in preterm infants. To further evaluate the benefits of fresh human milk in the NICU, a randomized controlled trial is needed. METHODS: Our prospective multicenter, double-blinded, randomized, controlled trial will include infants born at < 30 weeks' gestation and admitted to one of 29 tertiary NICUs in China. Infants in the intervention (fresh human milk) group (n = 1549) will receive at least two feeds of fresh human milk (i.e., within 4 h of expression) per day from the time of enrollment until 32 weeks' corrected age or discharge to home. Infants in the control group (n = 1549) will receive previously frozen human milk following the current standard protocols. Following informed consent, enrolled infants will be randomly allocated to the control or fresh human milk groups. The primary outcome is the composite outcome mortality or NEC ≥ stage 2 at 32 weeks' corrected age, and the secondary outcomes are mortality, NEC ≥ stage 2, NEC needing surgery, late-onset sepsis, retinopathy of prematurity (ROP), bronchopulmonary dysplasia (BPD), weight gain, change in weight, increase in length, increase in head circumference, time to full enteral feeds, and finally, the number and type of critical incident reports, including feeding errors. DISCUSSION: Our double-blinded, randomized, controlled trial aims to examine whether fresh human milk can improve infant outcomes. The results of this study will impact both Chinese and international medical practice and feeding policy for preterm infants. In addition, data from our study will inform changes in health policy in NICUs across China, such that mothers are encouraged to enter the NICU and express fresh milk for their infants. TRIAL REGISTRATION: Chinese Clinical Trial Registry; #ChiCTR1900020577; registered January 1, 2019; http://www.chictr.org.cn/showprojen.aspx?proj=34276.

Correction to: Predicting protein inter-residue contacts using composite likelihood maximization and deep learning.

Following publication of the original article [1], the author explained that there are several errors in the original article.

Predicting protein inter-residue contacts using composite likelihood maximization and deep learning.

BACKGROUND: Accurate prediction of inter-residue contacts of a protein is important to calculating its tertiary structure. Analysis of co-evolutionary events among residues has been proved effective in inferring inter-residue contacts. The Markov random field (MRF) technique, although being widely used for contact prediction, suffers from the following dilemma: the actual likelihood function of MRF is accurate but time-consuming to calculate; in contrast, approximations to the actual likelihood, say pseudo-likelihood, are efficient to calculate but inaccurate. Thus, how to achieve both accuracy and efficiency simultaneously remains a challenge. RESULTS: In this study, we present such an approach (called clmDCA) for contact prediction. Unlike plmDCA using pseudo-likelihood, i.e., the product of conditional probability of individual residues, our approach uses composite-likelihood, i.e., the product of conditional probability of all residue pairs. Composite likelihood has been theoretically proved as a better approximation to the actual likelihood function than pseudo-likelihood. Meanwhile, composite likelihood is still efficient to maximize, thus ensuring the efficiency of clmDCA. We present comprehensive experiments on popular benchmark datasets, including PSICOV dataset and CASP-11 dataset, to show that: i) clmDCA alone outperforms the existing MRF-based approaches in prediction accuracy. ii) When equipped with deep learning technique for refinement, the prediction accuracy of clmDCA was further significantly improved, suggesting the suitability of clmDCA for subsequent refinement procedure. We further present a successful application of the predicted contacts to accurately build tertiary structures for proteins in the PSICOV dataset. CONCLUSIONS: Composite likelihood maximization algorithm can efficiently estimate the parameters of Markov Random Fields and can improve the prediction accuracy of protein inter-residue contacts.