Initiatives to reduce postoperative surgical site infections of the head and neck cancer surgery with a special emphasis on developing countries

Introduction: Surgery in patients with head and neck cancers is frequently complicated by multiple stages of procedure that includes significant surgical removal of all or part of an organ with cancer, tissue reconstruction, and extensive neck dissection. Postoperative wound infections, termed ‘surgical site infections’ (SSIs) are a significant impediment to head-and-neck cancer surgery and recovery, and need to be addressed.

Areas covered: Approximately 10–45% of patients undergoing head-and-neck cancers surgery develop SSIs. SSIs can lead to delayed wound healing, increased morbidity and mortality as well as costs. Consequently, SSIs need to be avoided where possible, as even the surgery itself impacts on patients’ subsequent activities and their quality of life, which is exacerbated by SSIs. Several risk factors for SSIs need to be considered to reduce future rates, and care is also needed in the selection and duration of antibiotic prophylaxis.

Expert commentary: Head and neck surgeons should give personalized care especially to patients at high risk of SSIs. Such patients include those who have had chemoradiotherapy and need reconstructive surgery, and patients from lower and middle-income countries and from poorer communities in high income countries, who often have high levels of co-morbidity because of resource constraints.     

Learning to Discretize: Solving 1D Scalar Conservation Laws via Deep Reinforcement Learning

Conservation laws are considered to be fundamental laws of nature. It has broad applications in many fields, including physics, chemistry, biology, geology, and engineering. Solving the differential equations associated with conservation laws is a major branch in computational mathematics. The recent success of machine learning, especially deep learning in areas such as computer vision and natural language processing, has attracted a lot of attention from the community of computational mathematics and inspired many intriguing works in combining machine learning with traditional methods. In this paper, we are the first to view numerical PDE solvers as an MDP and to use (deep) RL to learn new solvers. As proof of concept, we focus on 1-dimensional scalar conservation laws. We deploy the machinery of deep reinforcement learning to train a policy network that can decide on how the numerical solutions should be approximated in a sequential and spatial-temporal adaptive manner. We will show that the problem of solving conservation laws can be naturally viewed as a sequential decision-making process, and the numerical schemes learned in such a way can easily enforce long-term accuracy. Furthermore, the learned policy network is carefully designed to determine a good local discrete approximation based on the current state of the solution, which essentially makes the proposed method a meta-learning approach. In other words, the proposed method is capable of learning how to discretize for a given situation mimicking human experts. Finally, we will provide details on how the policy network is trained, how well it performs compared with some state-of-the-art numerical solvers such as WENO schemes, and supervised learning based approach L3D and PINN, and how well it generalizes.     

Genome-wide haplotypic testing in a Finnish cohort identifies a novel association with low-density lipoprotein cholesterol

We performed genome-wide tests for association between haplotype clusters and each of 9 metabolic traits in a cohort of 5402 Northern Finnish individuals genotyped for 330 000 single-nucleotide polymorphisms. The metabolic traits were body mass index, C-reactive protein, diastolic blood pressure, glucose, high-density lipoprotein (HDL), insulin, low-density lipoprotein (LDL), systolic blood pressure, and triglycerides. Haplotype clusters were determined using Beagle. There were LDL-associated clusters in the chromosome 4q13.3-q21.1 region containing the albumin (ALB) and platelet factor 4 (PF4) genes. This region has not been associated with LDL in previous genome-wide association studies. The most significant haplotype cluster in this region was associated with 0.488 mmol/l higher LDL (95% CI: 0.361–0.615 mmol/l, P-value: 6.4 × 10⁻¹⁴). We also observed three previously reported associations: Chromosome 16q13 with HDL, chromosome 1p32.3-p32.2 with LDL and chromosome 19q13.31-q13.32 with LDL. The chromosome 1 and chromosome 4 LDL associations do not reach genome-wide significance in single-marker analyses of these data, illustrating the power of haplotypic association testing.     

桃红四物汤治疗雷诺氏病44例

近年来,笔者采用桃红四物汤治疗雷诺氏病44例,疗效满意,现报告如下. 一般资料 44例患者均为门诊病人,其中女性35例,男性9例;年龄16～58岁,平均26.7岁;病程1～6个月13例,7个月～1年22例,1年以上9例.主要症状均表现为手指皮肤苍白,继而青紫,先从手指尖开始,逐渐累及手指或全掌,呈典型的“手套征”,伴有局部冷、麻、针刺样疼痛或其他异常感觉,接触冷水或遇寒冷或情绪变化时发作,一般脉象沉细.本病发作十数分钟至1小时后症状可自行消失,严重者须保暖治疗（如将患指浸于温水中）方可缓解,皮肤转为潮红,然后恢复正常肤色.     

伴随结节性硬化的浸润性视网膜星形细胞瘤4例

众所周知，视网膜星形细胞瘤常伴有结节性硬化的表现。视网膜星形细胞瘤一般是发生在视网膜神经纤维层的无蒂轻度隆起性病变。临床上可以是单侧发生，也可以是双侧发生。可以单个病灶，也可以多个病灶。可以是透明的，也可以是不透明的。可以有钙化，也可以没有钙化。视网膜星形细胞瘤相对固定的伴有眼内结节性硬化，很少表现为浸润性增长。在很少的情况下视网膜星形细胞瘤可以表现出浸润性增长，造成局部严重并发症。     

Strategies to enhance transductional efficiency of adenoviral-based gene transfer to primary human fibroblasts and keratinocytes as a platform in dermal wounds

Genetically modified keratinocytes and fibroblasts are suitable for delivery of therapeutic genes capable of modifying the wound healing process. However, efficient gene delivery is a prerequisite for successful gene therapy of wounds. Whereas adenoviral vectors (Ads) exhibit superior levels of in vivo gene transfer, their transductional efficiency to cells resident within wounds may nonetheless be suboptimal, due to deficiency of the primary adenovirus receptor, coxsackie-adenovirus receptor (CAR). We explored CAR-independent transduction to fibroblasts and keratinocytes using a panel of CAR-independent fiber-modified Ads to determine enhancement of infectivity. These fiber-modified adenoviral vectors included Ad 3 knob (Ad5/3), canine Ad serotype 2 knob (Ad5CAV-2), RGD (Ad5.RGD), polylysine (Ad5.pK7), or both RGD and polylysine (Ad5.RGD.pK7). To evaluate whether transduction efficiencies of the fiber-modified adenoviral vectors correlated with the expression of their putative receptors on keratinocytes and fibroblasts, we analyzed the mRNA levels of CAR, alpha upsilon integrin, syndecan-1, and glypican-1 using quantitative polymerase chain reaction. Analysis of luciferase and green fluorescent protein transgene expression showed superior transduction efficiency of Ad5.pK7 in keratinocytes and Ad5.RGD.pK7 in fibroblasts. mRNA expression of alpha upsilon integrin, syndecan-1 and glypican-1 was significantly higher in primary fibroblasts than CAR. In keratinocytes, syndecan-1 expression was significantly higher than all the other receptors tested. Significant infectivity enhancement was achieved in keratinocytes and fibroblasts using fiber-modified adenoviral vectors. These strategies to enhance infectivity may help to achieve higher clinical efficacy of wound gene therapy.