Background: Light emitting‐diodes (LED) have various effects on living organisms and recent studies have shown the efficacy of visible light irradiation from LED for anticancer therapies. However, the mechanism of LED's effects on cancer cells remains unclear. The aim of the present study was to investigate the effects of LED on colon cancer cell lines and the role of photoreceptor Opsin 3 (Opn3) on LED irradiation in vitro.Methods: Human colon cancer cells (HT‐29 or HCT‐116) were seeded onto laboratory dishes and irradiated with 465‐nm LED at 30 mW/cm² for 30 minutes. Cell Counting Kit‐8 was used to measure cell viability, and apoptosis and caspase 3/8 expression were evaluated by AnnexinV/PI and reverse transcription‐polymerase chain reaction (RT‐PCR), respectively. Autophagy and expression of LC‐3 and beclin‐1 were also evaluated by autophagy assays, RT‐PCR and Western blotting. We further tested Opn3 knockdown by Opn3 siRNA and the G_i/o G‐protein inhibitor NF023 in these assays.Results: Viability of HT‐29 and HCT‐116 cells was lower in 465‐nm LED‐irradiated cultures than in control cultures. LC‐3 and beclin‐1 expressions were significantly higher in LED‐irradiated cultures, and autophagosomes were detected in irradiated cells. The reductive effect of cancer cell viability following blue LED irradiation was reversed by Opn3 knockdown or NF023 treatment. Furthermore, increased LC‐3 and beclin‐1 expression that resulted from blue LED irradiation was suppressed by Opn3 knockdown or NF023 treatment.Conclusion: Blue LED irradiation suppressed the growth of colon cancer cells and Opn3 may play an important role as a photoreceptor.

Influenza A viruses (IAVs) pose a serious global threat to humans and their livestock. This study aimed to determine the ideal irradiation by ultraviolet-light emitting diodes (UV-LEDs) for IAV disinfection. We irradiated the IAV H1N1 subtype with 4.8 mJ/cm² UV using eight UV-LEDs [peak wavelengths (WL) = 365, 310, 300, 290, 280, 270, and 260 nm)] or a mercury low pressure (LP)-UV lamp (Peak WL = 254 nm). Inactivation was evaluated by the infection ratio of Madin–Darby canine kidney (MDCK) cells or chicken embryonated eggs. Irradiation by the 260 nm UV-LED showed the highest inactivation among all treatments. Because the irradiation-induced inactivation effects strongly correlated with damage to viral RNA, we calculated the correlation coefficient (R_AE) between the irradiant spectrum and absorption of viral RNA. The R_AE scores strongly correlated with the inactivation by the UV-LEDs and LP-UV lamp. To increase the R_AE score, we combined three different peak WL UV-LEDs (hybrid UV-LED). The hybrid UV-LED (R_AE = 86.3) significantly inactivated both H1N1 and H6N2 subtypes to a greater extent than 260 nm (R_AE = 68.6) or 270 nm (R_AE = 42.2) UV-LEDs. The R_AE score is an important factor for increasing the virucidal effects of UV-LED irradiation.

Obstructive sleep apnea (OSA) is a serious disorder characterized by intermittent events of upper airway collapse during sleep. Snoring is the most common nocturnal symptom of OSA. Almost all OSA patients snore, but not all snorers have the disease. Recently, researchers have attempted to develop automated snore analysis technology for the purpose of OSA diagnosis. These technologies commonly require, as the first step, the automated identification of snore/breathing episodes (SBE) in sleep sound recordings. Snore intensity may occupy a wide dynamic range (> 95 dB) spanning from the barely audible to loud sounds. Low-intensity SBE sounds are sometimes seen buried within the background noise floor, even in high-fidelity sound recordings made within a sleep laboratory. The complexity of SBE sounds makes it a challenging task to develop automated snore segmentation algorithms, especially in the presence of background noise. In this paper, we propose a fundamentally novel approach based on artificial neural network (ANN) technology to detect SBEs. Working on clinical data, we show that the proposed method can detect SBE at a sensitivity and specificity exceeding 0.892 and 0.874 respectively, even when the signal is completely buried in background noise (SNR < 0 dB). We compare the performance of the proposed technology with those of the existing methods (short-term energy, zero-crossing rates) and illustrate that the proposed method vastly outperforms conventional techniques.

Approximately 10%–20% of adults and adolescents suffer from irritable bowel syndrome (IBS) worldwide. IBS is characterized by chronic gastrointestinal dysfunction which may reflect in altered motility. Currently, the diagnosis of IBS is made through expensive invasive radiographic and endoscopic examinations. However these are inconvenient and unsuited for community screening. Bowel sounds (BSs) can be easily recorded with non-invasive and low-cost equipment. Recently, several researchers have pointed out changes in features obtained from BS according to the pathological condition of bowel motility. However a widely accepted, simple automatic BS detection algorithm still has to be found, and the appropriate recording period needs to be investigated for further evaluation of bowel motility. In this study we propose a novel simple automatic method to detect the BSs based on the 3 dB bandwidth of the frequency peaks in the autoregressive moving average spectrum. We use the measure, sound-to-sound interval (SSI) obtained by the proposed method, to capture bowel motility. In this paper, we show that the proposed method for automatic detection could achieve a sensitivity of 87.8±5.88%, specificity of 91.7±4.33% and area under the curve of 0.923 when working on 16 healthy volunteers during mosapride administrations. Furthermore, we show that the measured SSI averaged over a period of 30 min can clearly capture bowel motility. Our findings should have the potential to contribute toward developing automated BS-based diagnosis of IBS.