Effect of Cryotherapy after Spine Surgery

Study DesignHistorical controlled trial.PurposeTo clarify the usefulness of cryotherapy after spine surgery.Overview of LiteratureCryotherapy has generally been performed subsequent to surgery on joints and in this application its clinical effects are well understood. However, cryotherapy has yet to be used following spine surgery. Its clinical efficacy in this context is unknown.MethodsThirty six patients had undergone one level microendoscopic surgery. Sixteen were enrolled into the cooling group, with the remaining 20 making up the no postoperative cryotherapy control group. Cryotherapy was performed at 5℃ using an icing system. A silicone balloon catheter with a thermo sensor on the tip was placed in the surgical wound. The temperature in the wound was recorded every 30 minutes until the next morning. The relationship between the depth of the sensor and the temperature in the wound were investigated using simple linear regression analysis. Laboratory data, visual analogue scale (VAS) for wound pain and postoperative bleeding were investigated.ResultsThe mean temperature in the surgical wound was 37.0 in the control group and 35.0℃ in the cooling group (p<0.001). There was a positive correlation between the depth of the thermo sensor and the temperature in the wound in the cooling group (y=0.91x+30.2, r=0.67, p=0.004). There were no significant differences between the groups in terms of laboratory data, VAS or postoperative bleeding.ConclusionsThe temperature in the wound was decreased significantly by spinal surgery cryotherapy

Finishing the euchromatic sequence of the human genome

The sequence of the human genome encodes the genetic instructions for human physiology, as well as rich information about human evolution. In 2001, the International Human Genome Sequencing Consortium reported a draft sequence of the euchromatic portion of the human genome. Since then, the international collaboration has worked to convert this draft into a genome sequence with high accuracy and nearly complete coverage. Here, we report the result of this finishing process. The current genome sequence (Build 35) contains 2.85 billion nucleotides interrupted by only 341 gaps. It covers ∼99% of the euchromatic genome and is accurate to an error rate of ∼1 event per 100,000 bases. Many of the remaining euchromatic gaps are associated with segmental duplications and will require focused work with new methods. The near-complete sequence, the first for a vertebrate, greatly improves the precision of biological analyses of the human genome including studies of gene number, birth and death. Notably, the human enome seems to encode only 20,000-25,000 protein-coding genes. The genome sequence reported here should serve as a firm foundation for biomedical research in the decades ahead

A quest for therapeutic antigens in bone and soft tissue sarcoma

Abstract Over the past three decades, there have been remarkable advances in the treatment of bone and soft tissue sarcomas. These include the introduction of adjuvant chemotherapy, establishment of guidelines for adequate surgical margins, and the development of post-excision reconstruction. There have also been advances in the field of immunotherapy against bone and soft tissue sarcomas, which, unfortunately, have received less attention. However, lack of progress in chemotherapy-based treatments for bone and soft tissue sarcomas has reignited interest in immunotherapeutic approaches. Here we summarize current progress in the immunotherapy of bone and soft tissue sarcomas including the strategies utilized to identify tumor-associated antigens, and the design of clinical trials.</p

Deep Learning-Based Nuclear Lobe Count Method for Differential Count of Neutrophils

Differentiating neutrophils based on the count of nuclear lobulation is useful for diagnosing various hematological disorders, including megaloblastic anemia, myelodysplastic syndrome, and sepsis. It has been reported that one-fifth of sepsis-infected patients worldwide died between 1990 and 2017. Notably, fewer nuclear-lobed and stab-formed neutrophils develop in the peripheral blood during sepsis. This abnormality can serve as an early diagnostic criterion. However, testing this feature is a complex and time-consuming task that is rife with human error. For this reason, we apply deep learning to automatically differentiate neutrophil and nuclear lobulation counts and report the world's first small-scale pilot. Blood films are prepared using venous peripheral blood taken from four healthy volunteers and are stained with May Grunwald Giemsa stain. Six-hundred 360 x 363-pixel images of neutrophils having five different nuclear lobulations are automatically captured by Cellavision DM-96, an automatic digital microscope camera. Images are input to an original architecture with five convolutional layers built on a deep learning neural-network platform by Sony, Neural Network Console. The deep learning system distinguishes the four groups (i.e., band-formed, two-, three-, and four- and five-segmented) of neutrophils with up to 99% accuracy, suggesting that neutrophils can be automatically differentiated based on their count of segmented nuclei using deep learning