Ovary and fimbrial stem cells: biology, niche and cancer origins.

The mammalian ovary is covered by a single-layered epithelium that undergoes rupture and remodelling following each ovulation. Although resident stem cells are presumed to be crucial for this cyclic regeneration, their identity and mode of action have been elusive. Surrogate stemness assays and in vivo fate-mapping studies using recently discovered stem cell markers have identified stem cell pools in the ovary and fimbria that ensure epithelial homeostasis. Recent findings provide insights into intrinsic mechanisms and local extrinsic cues that govern the function of ovarian and fimbrial stem cells. These discoveries have advanced our understanding of stem cell biology in the ovary and fimbria, and lay the foundations for evaluating the contribution of resident stem cells to the initiation and progression of human epithelial ovarian cancer.

Multi-vendor evaluation of artificial intelligence as an independent reader for double reading in breast cancer screening on 275,900 mammograms.

BACKGROUND: Double reading (DR) in screening mammography increases cancer detection and lowers recall rates, but has sustainability challenges due to workforce shortages. Artificial intelligence (AI) as an independent reader (IR) in DR may provide a cost-effective solution with the potential to improve screening performance. Evidence for AI to generalise across different patient populations, screening programmes and equipment vendors, however, is still lacking. METHODS: This retrospective study simulated DR with AI as an IR, using data representative of real-world deployments (275,900 cases, 177,882 participants) from four mammography equipment vendors, seven screening sites, and two countries. Non-inferiority and superiority were assessed for relevant screening metrics. RESULTS: DR with AI, compared with human DR, showed at least non-inferior recall rate, cancer detection rate, sensitivity, specificity and positive predictive value (PPV) for each mammography vendor and site, and superior recall rate, specificity, and PPV for some. The simulation indicates that using AI would have increased arbitration rate (3.3% to 12.3%), but could have reduced human workload by 30.0% to 44.8%. CONCLUSIONS: AI has potential as an IR in the DR workflow across different screening programmes, mammography equipment and geographies, substantially reducing human reader workload while maintaining or improving standard of care. TRIAL REGISTRATION: ISRCTN18056078 (20/03/2019; retrospectively registered).

Territory-wide population-based study of chronic hepatitis C infection and implications for hepatitis elimination in Hong Kong.

BACKGROUND: To study the epidemiology of chronic hepatitis C virus infection in Hong Kong and to estimate the service gap for achieving the WHO hepatitis elimination targets of attaining a diagnosis rate of 90%, treatment rate of 80% and 65% reduction in mortality rate by 2030. METHODS: From January 2005 to March 2017, patients who were tested positive for anti-HCV were retrospectively retrieved from all public hospitals in Hong Kong. The epidemiological data of 15 participating hospitals were analysed. RESULTS: A total of 11 309 anti-HCV+ patients were identified and the estimated diagnosis rate was 50.9%. Our HCV-infected patients were ageing (median age 59). The all-cause mortality rate increased from 26.2 to 54.8 per 1000 person-years over the last decade. Our estimated treatment rate was 12.4%. Among the treated patients, 93.6% had received pegylated interferon/ribavirin (Peg-IFN/RBV) but only 10.8% had received interferon-free direct-acting antivirals (DAAs). In a cohort of 1533 patients, 39% already had advanced liver fibrosis or cirrhosis. The sustained virological response rate for Peg-IFN/RBV and DAAs were 74.8% and 97.2% respectively. However, more than 70% of patients were not subjected to interferon treatment for various reasons. Patients who achieved SVR were associated with a significantly lower risk of HCC (4.7% vs 9.6%, P = 0.005) and death (1.7% vs 23.8%, P < 0.001). CONCLUSION: Our diagnosis rate, treatment rate and mortality rate reduction were still low, particularly the Peg-IFN outcomes, making it difficult to meet the WHO hepatitis elimination targets. A more generalized use of DAAs is urgently needed to improve the situation.

Minor class splicing shapes the zebrafish transcriptome during development.

Minor class or U12-type splicing is a highly conserved process required to remove a minute fraction of introns from human pre-mRNAs. Defects in this splicing pathway have recently been linked to human disease, including a severe developmental disorder encompassing brain and skeletal abnormalities known as Taybi-Linder syndrome or microcephalic osteodysplastic primordial dwarfism 1, and a hereditary intestinal polyposis condition, Peutz-Jeghers syndrome. Although a key mechanism for regulating gene expression, the impact of impaired U12-type splicing on the transcriptome is unknown. Here, we describe a unique zebrafish mutant, caliban (clbn), with arrested development of the digestive organs caused by an ethylnitrosourea-induced recessive lethal point mutation in the rnpc3 [RNA-binding region (RNP1, RRM) containing 3] gene. rnpc3 encodes the zebrafish ortholog of human RNPC3, also known as the U11/U12 di-snRNP 65-kDa protein, a unique component of the U12-type spliceosome. The biochemical impact of the mutation in clbn is the formation of aberrant U11- and U12-containing small nuclear ribonucleoproteins that impair the efficiency of U12-type splicing. Using RNA sequencing and microarrays, we show that multiple genes involved in various steps of mRNA processing, including transcription, splicing, and nuclear export are disrupted in clbn, either through intron retention or differential gene expression. Thus, clbn provides a useful and specific model of aberrant U12-type splicing in vivo. Analysis of its transcriptome reveals efficient mRNA processing as a critical process for the growth and proliferation of cells during vertebrate development.

Autophagy induction is a Tor- and Tp53-independent cell survival response in a zebrafish model of disrupted ribosome biogenesis.

Ribosome biogenesis underpins cell growth and division. Disruptions in ribosome biogenesis and translation initiation are deleterious to development and underlie a spectrum of diseases known collectively as ribosomopathies. Here, we describe a novel zebrafish mutant, titania (tti(s450)), which harbours a recessive lethal mutation in pwp2h, a gene encoding a protein component of the small subunit processome. The biochemical impacts of this lesion are decreased production of mature 18S rRNA molecules, activation of Tp53, and impaired ribosome biogenesis. In tti(s450), the growth of the endodermal organs, eyes, brain, and craniofacial structures is severely arrested and autophagy is up-regulated, allowing intestinal epithelial cells to evade cell death. Inhibiting autophagy in tti(s450) larvae markedly reduces their lifespan. Somewhat surprisingly, autophagy induction in tti(s450) larvae is independent of the state of the Tor pathway and proceeds unabated in Tp53-mutant larvae. These data demonstrate that autophagy is a survival mechanism invoked in response to ribosomal stress. This response may be of relevance to therapeutic strategies aimed at killing cancer cells by targeting ribosome biogenesis. In certain contexts, these treatments may promote autophagy and contribute to cancer cells evading cell death.

Subjective measures in sit-to-stand task in post-stroke hemiparesis: comment on Brière, Nadeau, Lauzière, and Gravel (2013).

In a recent study, Brière, Nadeau, Lauzière, and Gravel (2013 ) examined the perceptions of individuals post-stroke on their weight-bearing distribution and knee effort distribution in sit-to-stand tasks. The present comment emphasized the importance of the feeling of post-stroke hemiparesis in sit-to-stand task and identified areas of improvements of the target study.

Positive Effects of Guanidinium Salt Post-Treatment on Multi-Cation Mixed Halide Perovskite Solar Cells.

As one of the most promising photovoltaic technologies, perovskite solar cells (PSCs) exhibit high absorption coefficients, tunable bandgaps, large carrier mobilities, and versatile fabrication techniques. Nevertheless, the commercialization of the technology is hindered by poor material stability, short device lifetimes and the scalability of fabrication techniques. To address these technological drawbacks, various strategies have been explored, with one particularly promising approach involving the formation of a low-dimensional layer on the surface of the three-dimensional perovskite film. In this work, we demonstrate the use of guanidinium tetrafluoroborate, CH6BF4N3, (GATFB) as a post-treatment step to enhance the performance of PSCs. Compared with the control sample, the application of GATFB improves the film surface topology, reduces surface defects, suppresses non-radiative recombination, and optimizes band alignment within the device. These positive effects reduce recombination losses and enhance charge transport in the device, resulting in PSCs with an open-circuit voltage (VOC) of 1.18 V and a power conversion efficiency (PCE) of 19.7%. The results obtained in this work exhibit the potential of integrating low-dimensional structures in PSCs as an effective approach to enhance the overall device performance, providing useful information for further advancement in this rapidly evolving field of photovoltaic technology.

Hydrophobic and Luminescent Polydimethylsiloxane PDMS-Y2O3:Eu3+ Coating for Power Enhancement and UV Protection of Si Solar Cells.

Solar cells have been developed as a highly efficient source of alternative energy, collecting photons from sunlight and turning them into electricity. On the other hand, ultraviolet (UV) radiation has a substantial impact on solar cells by damaging their active layers and, as a result, lowering their efficiency. Potential solutions include the blocking of UV light (which can reduce the power output of solar cells) or converting UV photons into visible light using down-conversion optical materials. In this work, we propose a novel hydrophobic coating based on a polydimethylsiloxane (PDMS) layer with embedded red emitting Y2O3:Eu3+ (quantum yield = 78.3%) particles for UV radiation screening and conversion purposes. The favorable features of the PDMS-Y2O3:Eu3+ coating were examined using commercially available polycrystalline silicon solar cells, resulting in a notable increase in the power conversion efficiency (PCE) by ~9.23%. The chemical and UV stability of the developed coatings were assessed by exposing them to various chemical conditions and UV irradiation. It was found that the developed coating can endure tough environmental conditions, making it potentially useful as a UV-protective, water-repellent, and efficiency-enhancing coating for solar cells.

Two-Step Perovskite Solar Cells with > 25% Efficiency: Unveiling the Hidden Bottom Surface of Perovskite Layer.

While significant efforts in surface engineering have been devoted to the conversion process of lead iodide (PbI2) into perovskite and top surface engineering of perovskite layer with remarkable progress, the exploration of residual PbI2 clusters and the hidden bottom surface on perovskite layer have been limited. In this work, a new strategy involving 1-butyl-3-methylimidazolium acetate (BMIMAc) ionic liquid (IL) additives is developed and it is found that both the cations and the anions in ILs can interact with the perovskite components, thereby regulating the crystallization process and diminishing the residue PbI2 clusters as well as filling vacancies. The introduction of BMIMAc ILs induces the formation of a uniform porous PbI2 film, facilitating better penetration of the second-step organic salt and fostering a more extensive interaction between PbI2 and the organic salt. Surprisingly, the oversized residual PbI2 clusters at the bottom surface of the perovskite layer completely diminish. In addition, advanced depth analysis techniques including depth-resolved grazing-incidence wide-angle X-ray scattering (GIWAXS) and bottom thinning technology are employed for a comprehensive understanding of the reduction in residual PbI2. Leveraging effective PbI2 management and regulation of the perovskite crystallization process, the champion devices achieve a power conversion efficiency (PCE) of 25.06% with long-term stability.

Stepping toward Portable Optoelectronics with SnO2 Quantum Dot-Based Electron Transport Layers.

With a power conversion efficiency (PCE) of more than 25%, perovskite solar cells (PSCs) have shown an immense potential application for solar energy conversion. Owing to lower manufacturing costs and facile processibility via printing techniques, PSCs can easily be scaled up to an industrial scale. The device performance of printed PSCs has been improving steadily with the development and optimization of the printing process for the device functional layers. Various kinds of SnO2 nanoparticle (NP) dispersion solutions including commercial ones are used to print the electron transport layer (ETL) of printed PSCs, and high processing temperatures are often required to obtain ETLs with optimum quality. This, however, limits the application of SnO2 ETLs in printed and flexible PSCs. In this work, the use of an alternative SnO2 dispersion solution based on SnO2 quantum dots (QDs) to fabricate ETLs of printed PSCs on flexible substrates is reported. A comparative analysis of the performance and properties of the obtained devices with the devices fabricated employing ETLs made with a commercial SnO2 NP dispersion solution is carried out. The ETLs made with SnO2 QDs are shown to improve the performance of devices by ∼11% on average compared to the ETLs made with SnO2 NPs. It is found that employing SnO2 QDs can reduce trap states in the perovskite layer and improve charge extraction in devices.

Comparing Prognostic Factors of Cancers Identified by Artificial Intelligence (AI) and Human Readers in Breast Cancer Screening.

Invasiveness status, histological grade, lymph node stage, and tumour size are important prognostic factors for breast cancer survival. This evaluation aims to compare these features for cancers detected by AI and human readers using digital mammography. Women diagnosed with breast cancer between 2009 and 2019 from three UK double-reading sites were included in this retrospective cohort evaluation. Differences in prognostic features of cancers detected by AI and the first human reader (R1) were assessed using chi-square tests, with significance at p < 0.05. From 1718 screen-detected cancers (SDCs) and 293 interval cancers (ICs), AI flagged 85.9% and 31.7%, respectively. R1 detected 90.8% of SDCs and 7.2% of ICs. Of the screen-detected cancers detected by the AI, 82.5% had an invasive component, compared to 81.1% for R1 (p-0.374). For the ICs, this was 91.5% and 93.8% for AI and R1, respectively (p = 0.829). For the invasive tumours, no differences were found for histological grade, tumour size, or lymph node stage. The AI detected more ICs. In summary, no differences in prognostic factors were found comparing SDC and ICs identified by AI or human readers. These findings support a potential role for AI in the double-reading workflow.

Prospective implementation of AI-assisted screen reading to improve early detection of breast cancer.

Artificial intelligence (AI) has the potential to improve breast cancer screening; however, prospective evidence of the safe implementation of AI into real clinical practice is limited. A commercially available AI system was implemented as an additional reader to standard double reading to flag cases for further arbitration review among screened women. Performance was assessed prospectively in three phases: a single-center pilot rollout, a wider multicenter pilot rollout and a full live rollout. The results showed that, compared to double reading, implementing the AI-assisted additional-reader process could achieve 0.7-1.6 additional cancer detection per 1,000 cases, with 0.16-0.30% additional recalls, 0-0.23% unnecessary recalls and a 0.1-1.9% increase in positive predictive value (PPV) after 7-11% additional human reads of AI-flagged cases (equating to 4-6% additional overall reading workload). The majority of cancerous cases detected by the AI-assisted additional-reader process were invasive (83.3%) and small-sized (≤10 mm, 47.0%). This evaluation suggests that using AI as an additional reader can improve the early detection of breast cancer with relevant prognostic features, with minimal to no unnecessary recalls. Although the AI-assisted additional-reader workflow requires additional reads, the higher PPV suggests that it can increase screening effectiveness.

Artificial Intelligence as Supporting Reader in Breast Screening: A Novel Workflow to Preserve Quality and Reduce Workload.

OBJECTIVE: To evaluate the effectiveness of a new strategy for using artificial intelligence (AI) as supporting reader for the detection of breast cancer in mammography-based double reading screening practice. METHODS: Large-scale multi-site, multi-vendor data were used to retrospectively evaluate a new paradigm of AI-supported reading. Here, the AI served as the second reader only if it agrees with the recall/no-recall decision of the first human reader. Otherwise, a second human reader made an assessment followed by the standard clinical workflow. The data included 280 594 cases from 180 542 female participants screened for breast cancer at seven screening sites in two countries and using equipment from four hardware vendors. The statistical analysis included non-inferiority and superiority testing of cancer screening performance and evaluation of the reduction in workload, measured as arbitration rate and number of cases requiring second human reading. RESULTS: Artificial intelligence as a supporting reader was found to be superior or noninferior on all screening metrics compared with human double reading while reducing the number of cases requiring second human reading by up to 87% (245 395/280 594). Compared with AI as an independent reader, the number of cases referred to arbitration was reduced from 13% (35 199/280 594) to 2% (5056/280 594). CONCLUSION: The simulation indicates that the proposed workflow retains screening performance of human double reading while substantially reducing the workload. Further research should study the impact on the second human reader because they would only assess cases in which the AI prediction and first human reader disagree.

Effects of display factors on Chinese proofreading performance and preferences.

This experiment investigated the influence of four display factors, viz. typeface (Ming Liu and Jheng Hei), font size (10-point and 14-point), text direction (horizontal and vertical) and copy placement (left-right and top-bottom) on Chinese proofreading performance and subjective preferences. Proofreading performance was measured in terms of time and accuracy, and preferences were examined in terms of comfort, ease and fatigue. It was found that the horizontal text direction resulted in faster proofreading than the vertical one, but the other three factors were non significant. The faster proofreading using the horizontal text direction was valid for both the left-right and the top-bottom arrangements. There was an interaction between typeface and font size such that, for the smaller character size (10-point), the Ming Liu style produced a faster performance than the Jheng Hei style. However, for the larger character size (14-point), the Jheng Hei style produced a faster performance than the Ming Liu style. Regarding proofreading accuracy, the number of non-detected missing words and related false alarm in left-right copy placement setting was significantly larger than that in top-down setting. Greater accuracy was obtained in proofreading at the cost of a speed-accuracy trade-off--the higher the accuracy in the proofreading task, the longer the proofreading time. Font size and text direction had significant effects on proofreading preferences, but typeface and copy placement did not. Practitioner Summary: This study examined four display factors on Chinese proofreading performance and subjective preferences. The findings of this experiment provide information and recommendations that should prove useful for the design of proofreading interfaces to improve proofreading time and accuracy, and to satisfy proofreaders' preference.

The guessing of mine safety signs meaning: effects of user factors and cognitive sign features.

This study investigated the effects of user factors and cognitive sign features on the guessability of mine safety signs. Sixty naïve participants guessed the meanings and rated the cognitive sign features of 42 Mainland Chinese mine safety signs. The results showed that some user factors were significant predictors of guessing performance, while some were not. As expected, guessability scores varied significantly with the cognitive sign features of familiarity, concreteness, simplicity, meaningfulness and semantic closeness. The findings emphasize the need to create awareness of the importance of mine safety and promote understanding of mine safety sign meanings amongst people in their work environments. To design more user-friendly mine safety signs, industrial designers should develop and evaluate signs with consideration of the significant user factors and the 5 sign features tested here.

A Morphological Study of Solvothermally Grown SnO2 Nanostructures for Application in Perovskite Solar Cells.

Tin(IV) oxide (SnO2) nanostructures, which possess larger surface areas for transporting electron carriers, have been used as an electron transport layer (ETL) in perovskite solar cells (PSCs). However, the reported power conversion efficiencies (PCEs) of this type of PSCs show a large variation. One of the possible reasons for this phenomenon is the low reproducibility of SnO2 nanostructures if they are prepared by different research groups using various growth methods. This work focuses on the morphological study of SnO2 nanostructures grown by a solvothermal method. The growth parameters including growth pressure, substrate orientation, DI water-to-ethanol ratios, types of seed layer, amount of acetic acid, and growth time have been systematically varied. The SnO2 nanomorphology exhibits a different degree of sensitivity and trends towards each growth factor. A surface treatment is also required for solvothermally grown SnO2 nanomaterials for improving photovoltaic performance of PSCs. The obtained results in this work provide the research community with an insight into the general trend of morphological changes in SnO2 nanostructures influenced by different solvothermal growth parameters. This information can guide the researchers to prepare more reproducible solvothermally grown SnO2 nanomaterials for future application in devices.

Solution-Processed SnO2 Quantum Dots for the Electron Transport Layer of Flexible and Printed Perovskite Solar Cells.

Flexible and printed perovskite solar cells (PSCs) fabricated on lightweight plastic substrates have many excellent potential applications in emerging new technologies including wearable and portable electronics, the internet of things, smart buildings, etc. To fabricate flexible and printed PSCs, all of the functional layers of devices should be processed at low temperatures. Tin oxide is one of the best metal oxide materials to employ as the electron transport layer (ETL) in PSCs. Herein, the synthesis and application of SnO2 quantum dots (QDs) to prepare the ETL of flexible and printed PSCs are demonstrated. SnO2 QDs are synthesized via a solvothermal method and processed to obtain aqueous and printable ETL ink solutions with different QD concentrations. PSCs are fabricated using a slot-die coating method on flexible plastic substrates. The solar cell performance and spectral response of the obtained devices are characterized using a solar simulator and an external quantum efficiency measurement system. The ETLs prepared using 2 wt% SnO2 QD inks are found to produce devices with a high average power conversion efficiency (PCE) along with a 10% PCE for a champion device. The results obtained in this work provide the research community with a method to prepare fully solution-processed SnO2 QD-based inks that are suitable for the deposition of SnO2 ETLs for flexible and printed PSCs.

Physical properties of carbon nanowalls synthesized by the ICP-PECVD method vs. the growth time.

Investigation of the physical properties of carbon nanowall (CNW) films is carried out in correlation with the growth time. The structural, electronic, optical and electrical properties of CNW films are investigated using electron microscopy, Raman spectroscopy, X-ray photoelectron spectroscopy, ultraviolet photoelectron spectroscopy, UV-Vis spectroscopy, Hall Effect measurement system, Four Point Probing system, and thermoelectric measurements. Shorter growth time results in thinner CNW films with a densely spaced labyrinth structure, while a longer growth time results in thicker CNW films with a petal structure. These changes in morphology further lead to changes in the structural, optical, and electrical properties of the CNW.

Room-temperature multiple ligands-tailored SnO2 quantum dots endow in situ dual-interface binding for upscaling efficient perovskite photovoltaics with high VOC.

The benchmark tin oxide (SnO2) electron transporting layers (ETLs) have enabled remarkable progress in planar perovskite solar cell (PSCs). However, the energy loss is still a challenge due to the lack of "hidden interface" control. We report a novel ligand-tailored ultrafine SnO2 quantum dots (QDs) via a facile rapid room temperature synthesis. Importantly, the ligand-tailored SnO2 QDs ETL with multi-functional terminal groups in situ refines the buried interfaces with both the perovskite and transparent electrode via enhanced interface binding and perovskite passivation. These novel ETLs induce synergistic effects of physical and chemical interfacial modulation and preferred perovskite crystallization-directing, delivering reduced interface defects, suppressed non-radiative recombination and elongated charge carrier lifetime. Power conversion efficiency (PCE) of 23.02% (0.04 cm2) and 21.6% (0.98 cm2, VOC loss: 0.336 V) have been achieved for the blade-coated PSCs (1.54 eV Eg) with our new ETLs, representing a record for SnO2 based blade-coated PSCs. Moreover, a substantially enhanced PCE (VOC) from 20.4% (1.15 V) to 22.8% (1.24 V, 90 mV higher VOC, 0.04 cm2 device) in the blade-coated 1.61 eV PSCs system, via replacing the benchmark commercial colloidal SnO2 with our new ETLs.

Very-low-bandgap metallopolyynes of platinum with a cyclopentadithiophenone ring for organic solar cells absorbing down to the near-infrared spectral region.

Two solution-processable metallopolyynes of platinum functionalized with the electron-deficient 4H-cyclopenta[2,1-b:3,4-b']dithiophen-4-one spacer and their model molecular complexes were synthesized and developed for the applications of polymer solar cells. These metallated polymers possess extremely low bandgaps of 1.44-1.53 eV which extend toward the near-infrared (NIR) range of the solar spectrum, and represent the lowest optical bandgap yet reported for platinum(II) metallopolyynes to date. The structural flexibility, processibility, and good photovoltaic performance make cyclopentadithiophenone-containing polymers prominent candidates for NIR photovoltaic applications.