Thousands of conductance levels in memristors integrated on CMOS.

Neural networks based on memristive devices1-3 have the ability to improve throughput and energy efficiency for machine learning4,5 and artificial intelligence6, especially in edge applications7-21. Because training a neural network model from scratch is costly in terms of hardware resources, time and energy, it is impractical to do it individually on billions of memristive neural networks distributed at the edge. A practical approach would be to download the synaptic weights obtained from the cloud training and program them directly into memristors for the commercialization of edge applications. Some post-tuning in memristor conductance could be done afterwards or during applications to adapt to specific situations. Therefore, in neural network applications, memristors require high-precision programmability to guarantee uniform and accurate performance across a large number of memristive networks22-28. This requires many distinguishable conductance levels on each memristive device, not only laboratory-made devices but also devices fabricated in factories. Analog memristors with many conductance states also benefit other applications, such as neural network training, scientific computing and even 'mortal computing'25,29,30. Here we report 2,048 conductance levels achieved with memristors in fully integrated chips with 256 × 256 memristor arrays monolithically integrated on complementary metal-oxide-semiconductor (CMOS) circuits in a commercial foundry. We have identified the underlying physics that previously limited the number of conductance levels that could be achieved in memristors and developed electrical operation protocols to avoid such limitations. These results provide insights into the fundamental understanding of the microscopic picture of memristive switching as well as approaches to enable high-precision memristors for various applications. Fig. 1 HIGH-PRECISION MEMRISTOR FOR NEUROMORPHIC COMPUTING.: a, Proposed scheme of the large-scale application of memristive neural networks for edge computing. Neural network training is performed in the cloud. The obtained weights are downloaded and accurately programmed into a massive number of memristor arrays distributed at the edge, which imposes high-precision requirements on memristive devices. b, An eight-inch wafer with memristors fabricated by a commercial semiconductor manufacturer. c, High-resolution transmission electron microscopy image of the cross-section view of a memristor. Pt and Ta serve as the bottom electrode (BE) and top electrode (TE), respectively. Scale bars, 1 µm and 100 nm (inset). d, Magnification of the memristor material stack. Scale bar, 5 nm. e, As-programmed (blue) and after-denoising (red) currents of a memristor are read by a constant voltage (0.2 V). The denoising process eliminated the large-amplitude RTN observed in the as-programmed state (see Methods). f, Magnification of three nearest-neighbour states after denoising. The current of each state was read by a constant voltage (0.2 V). No large-amplitude RTN was observed, and all of the states can be clearly distinguished. g, An individual memristor on the chip was tuned into 2,048 resistance levels by high-resolution off-chip driving circuitry, and each resistance level was read by a d.c. voltage sweeping from 0 to 0.2 V. The target resistance was set from 50 µS to 4,144 µS with a 2-µS interval between neighbouring levels. All readings at 0.2 V are less than 1 µS from the target conductance. Bottom inset, magnification of the resistance levels. Top inset, experimental results of an entire 256 × 256 array programmed by its 6-bit on-chip circuitry into 64 32 × 32 blocks, and each block is programmed into one of the 64 conductance levels. Each of the 256 × 256 memristors has been previously switched over one million cycles, demonstrating the high endurance and robustness of the devices.

Polymer-like Inorganic Double Helical van der Waals Semiconductor.

In high-performance flexible and stretchable electronic devices, conventional inorganic semiconductors made of rigid and brittle materials typically need to be configured into geometrically deformable formats and integrated with elastomeric substrates, which leads to challenges in scaling down device dimensions and complexities in device fabrication and integration. Here we report the extraordinary mechanical properties of the newly discovered inorganic double helical semiconductor tin indium phosphate. This spiral-shape double helical crystal shows the lowest Young's modulus (13.6 GPa) among all known stable inorganic materials. The large elastic (>27%) and plastic (>60%) bending strains are also observed and attributed to the easy slippage between neighboring double helices that are coupled through van der Waals interactions, leading to the high flexibility and deformability among known semiconducting materials. The results advance the fundamental understanding of the unique polymer-like mechanical properties and lay the foundation for their potential applications in flexible electronics and nanomechanics disciplines.

Demographic, clinical, and biochemical predictors of pica in high-intensity blood donors.

BACKGROUND: Frequent blood donors who contribute multiple times annually are important for maintaining an adequate blood supply. However, repeated donations exacerbate iron deficiency, which can lead to pica, a condition characterised as repeated eating or chewing of a non-nutritious substance such as ice, clay and dirt. Understanding characteristics of frequent donors that are associated with increased risk for developing pica will help to identify them and prevent this adverse consequence of blood donation. METHODS: Demographic, clinical, haematological, and biochemical factors associated with pica were investigated using univariable and multivariable logistic regression analysis in a cohort of 1693 high-intensity donors who gave nine or more units of whole blood in the preceding 2 years. Pica was classified by questionnaire responses as consuming at least 8 oz of ice daily and/or consumption of non-ice substances regardless of the amount and frequency. RESULTS: Pica was present in 1.5% of the high-intensity donors, and only occurred in those with ferritin <50 ng/ml. Of 16 candidate variables, only haematocrit (OR = 0.835, p = 0.020) was independently associated with pica. Although severe iron deficiency was more prevalent in high-intensity donors, pica behaviours were less prevalent than in less frequent donors (2.2%). CONCLUSION: We have uncovered predictors of pica in high-intensity donors, which further emphasises the need to continue to implement iron replacement programs to reduce the prevalence of pica and maintain a robust pool of frequent donors.

A Tantalum Disulfide Charge-Density-Wave Stochastic Artificial Neuron for Emulating Neural Statistical Properties.

Artificial neuronal devices that functionally resemble biological neurons are important toward realizing advanced brain emulation and for building bioinspired electronic systems. In this Communication, the stochastic behaviors of a neuronal oscillator based on the charge-density-wave (CDW) phase transition of a 1T-TaS2 thin film are reported, and the capability of this neuronal oscillator to generate spike trains with statistical features closely matching those of biological neurons is demonstrated. The stochastic behaviors of the neuronal device result from the melt-quench-induced reconfiguration of CDW domains during each oscillation cycle. Owing to the stochasticity, numerous key features of the Hodgkin-Huxley description of neurons can be realized in this compact two-terminal neuronal oscillator. A statistical analysis of the spike train generated by the artificial neuron indicates that it resembles the neurons in the superior olivary complex of a mammalian nervous system, in terms of its interspike interval distribution, the time-correlation of spiking behavior, and its response to acoustic stimuli.

Demographic, clinical, and biochemical predictors of pica in a large cohort of blood donors.

BACKGROUND: Pica is characterized as repeatedly eating or chewing a non-nutritious substance including, but not limited to ice, clay and dirt, starch, raw pasta, chalk, coal, paint, or paper. Pica symptoms can be intense and addiction-like and disrupt quality of life. It is strongly linked to iron deficiency. Since substantial iron loss occurs during blood donation, blood donors may be susceptible to development of pica behaviors. METHODS: We investigated demographic, clinical, hematological, and biochemical factors associated with pica using univariable and multivariable logistic regression analysis in a cohort of 11,418 racially diverse blood donors. Pica was defined by questionnaire responses as consuming at least 8 oz of ice daily and/or consumption of non-ice substances regardless of the amount and frequency. RESULTS: Pica was present in 2.2% of the donors. The sensitivity and specificity of pica in iron-deficient donors were 36% and 82%, respectively. Lower ferritin (p = .001), non-Asian race (p < .001), higher red cell distribution width (p < .001), younger age, and restless legs syndrome (p = .008) were independently associated with pica. Female sex is associated with iron deficiency but was not an independent predictor of pica suggesting that iron deficient males and females were equally susceptible to the development of pica behaviors. Donors with normal ferritin levels also reported pica, reinforcing the role of non-iron related factors in its presentation. CONCLUSIONS: We have identified demographic, clinical, and biochemical predictors of pica that help identify those most at risk for developing pica behaviors, and thereby assist in its clinical diagnosis and treatment.

A brief review of common mathematical calculations in therapeutic apheresis.

Apheresis is defined as the removal of blood from the body, its separation into constituent components, and removal or manipulation of one of these components prior to intravascular return with or without the addition of replacement fluid. Patients undergoing therapeutic apheresis often have multiple comorbidities, potentially affecting their hemodynamic status. Thus, a thorough understanding of apheresis principles and calculations is required for the performance of safe, efficacious, and successful procedures. The performance of simple transfusions or red blood cell exchange procedures is additionally complicated by the difficulties inherent in the procurement of compatible blood products, and the emphasis on minimizing exposure to unnecessary blood products. It is essential that apheresis physicians be able to accurately evaluate the risks/benefits inherent in the procedural options and efficiently stratify patients to the optimal therapeutic modality. The formulas requisite for performing therapeutic apheresis calculations are herein reviewed.

Highly Oriented Low-Dimensional Tin Halide Perovskites with Enhanced Stability and Photovoltaic Performance.

The low toxicity and a near-ideal choice of bandgap make tin perovskite an attractive alternative to lead perovskite in low cost solar cells. However, the development of Sn perovskite solar cells has been impeded by their extremely poor stability when exposed to oxygen. We report low-dimensional Sn perovskites that exhibit markedly enhanced air stability in comparison with their 3D counterparts. The reduced degradation under air exposure is attributed to the improved thermodynamic stability after dimensional reduction, the encapsulating organic ligands, and the compact perovskite film preventing oxygen ingress. We then explore these highly oriented low-dimensional Sn perovskite films in solar cells. The perpendicular growth of the perovskite domains between electrodes allows efficient charge carrier transport, leading to power conversion efficiencies of 5.94% without the requirement of further device structure engineering. We tracked the performance of unencapsulated devices over 100 h and found no appreciable decay in efficiency. These findings raise the prospects of pure Sn perovskites for solar cells application.

Review of Deep Learning Based Autosegmentation for Clinical Target Volume: Current Status and Future Directions.

Purpose: Manual contour work for radiation treatment planning takes significant time to ensure volumes are accurately delineated. The use of artificial intelligence with deep learning based autosegmentation (DLAS) models has made itself known in recent years to alleviate this workload. It is used for organs at risk contouring with significant consistency in performance and time saving. The purpose of this study was to evaluate the performance of present published data for DLAS of clinical target volume (CTV) contours, identify areas of improvement, and discuss future directions. Methods and Materials: A literature review was performed by using the key words "deep learning" AND ("segmentation" or "delineation") AND "clinical target volume" in an indexed search into PubMed. A total of 154 articles based on the search criteria were reviewed. The review considered the DLAS model used, disease site, targets contoured, guidelines used, and the overall performance. Results: Of the 53 articles investigating DLAS of CTV, only 6 were published before 2020. Publications have increased in recent years, with 46 articles published between 2020 and 2023. The cervix (n = 19) and the prostate (n = 12) were studied most frequently. Most studies (n = 43) involved a single institution. Median sample size was 130 patients (range, 5-1052). The most common metrics used to measure DLAS performance were Dice similarity coefficient followed by Hausdorff distance. Dosimetric performance was seldom reported (n = 11). There was also variability in specific guidelines used (Radiation Therapy Oncology Group (RTOG), European Society for Therapeutic Radiology and Oncology (ESTRO), and others). DLAS models had good overall performance for contouring CTV volumes for multiple disease sites, with most studies showing Dice similarity coefficient values >0.7. DLAS models also delineated CTV volumes faster compared with manual contouring. However, some DLAS model contours still required at least minor edits, and future studies investigating DLAS of CTV volumes require improvement. Conclusions: DLAS demonstrates capability of completing CTV contour plans with increased efficiency and accuracy. However, most models are developed and validated by single institutions using guidelines followed by the developing institutions. Publications about DLAS of the CTV have increased in recent years. Future studies and DLAS models need to include larger data sets with different patient demographics, disease stages, validation in multi-institutional settings, and inclusion of dosimetric performance.

Ultralow Power In-Sensor Neuronal Computing with Oscillatory Retinal Neurons for Frequency-Multiplexed, Parallel Machine Vision.

In-sensor and near-sensor computing architectures enable multiply accumulate operations to be carried out directly at the point of sensing. In-sensor architectures offer dramatic power and speed improvements over traditional von Neumann architectures by eliminating multiple analog-to-digital conversions, data storage, and data movement operations. Current in-sensor processing approaches rely on tunable sensors or additional weighting elements to perform linear functions such as multiply accumulate operations as the sensor acquires data. This work implements in-sensor computing with an oscillatory retinal neuron device that converts incident optical signals into voltage oscillations. A computing scheme is introduced based on the frequency shift of coupled oscillators that enables parallel, frequency multiplexed, nonlinear operations on the inputs. An experimentally implemented 3 × 3 focal plane array of coupled neurons shows that functions approximating edge detection, thresholding, and segmentation occur in parallel. An example of inference on handwritten digits from the MNIST database is also experimentally demonstrated with a 3 × 3 array of coupled neurons feeding into a single hidden layer neural network, approximating a liquid-state machine. Finally, the equivalent energy consumption to carry out image processing operations, including peripherals such as the Fourier transform circuits, is projected to be <20 fJ/OP, possibly reaching as low as 15 aJ/OP.

Study of Bayesian variable selection method on mixed linear regression models.

Variable selection has always been an important issue in statistics. When a linear regression model is used to fit data, selecting appropriate explanatory variables that strongly impact the response variables has a significant effect on the model prediction accuracy and interpretation effect. redThis study introduces the Bayesian adaptive group Lasso method to solve the variable selection problem under a mixed linear regression model with a hidden state and explanatory variables with a grouping structure. First, the definition of the implicit state mixed linear regression model is presented. Thereafter, the Bayesian adaptive group Lasso method is used to determine the penalty function and parameters, after which each parameter's specific form of the fully conditional posterior distribution is calculated. Moreover, the Gibbs algorithm design is outlined. Simulation experiments are conducted to compare the variable selection and parameter estimation effects in different states. Finally, a dataset of Alzheimer's Disease is used for application analysis. The results demonstrate that the proposed method can identify the observation from different hidden states, but the results of the variable selection in different states are obviously different.

Review of cone beam computed tomography based online adaptive radiotherapy: current trend and future direction.

Adaptive radiotherapy (ART) was introduced in the late 1990s to improve the accuracy and efficiency of therapy and minimize radiation-induced toxicities. ART combines multiple tools for imaging, assessing the need for adaptation, treatment planning, quality assurance, and has been utilized to monitor inter- or intra-fraction anatomical variations of the target and organs-at-risk (OARs). Ethos™ (Varian Medical Systems, Palo Alto, CA), a cone beam computed tomography (CBCT) based radiotherapy treatment system that uses artificial intelligence (AI) and machine learning to perform ART, was introduced in 2020. Since then, numerous studies have been done to examine the potential benefits of Ethos™ CBCT-guided ART compared to non-adaptive radiotherapy. This review will explore the current trends of Ethos™, including improved CBCT image quality, a feasible clinical workflow, daily automated contouring and treatment planning, and motion management. Nevertheless, evidence of clinical improvements with the use of Ethos™ are limited and is currently under investigation via clinical trials.

Improvement in plan quality after Implementation of clinical goals in a large network of cancer centers.

Clinical Goals (CG) is a tool available in the Varian Eclipse planning system to objectively and visually evaluate the quality of treatment plans based upon user-defined dose-volume parameters. We defined a set of CG for Stereotactic Radiosurgery (SRS) and Intensity-Modulated Radiotherapy (IMRT) based on published data and guidelines and implemented this in a network of cancer centers in India (American Institute of Oncology). A dosimetric study was performed to compare brain SRS and breast IMRT plan quality before and after CG implementation.The CG defined for SRS plans were target V100% ≥ 98%, dose gradient measure (GM) ≤ 0.5 cm, conformity index (CI) 1.0 to 1.2. For breast IMRT plans, CG defined target V100% ≥ 97%, V95% ≥ 95%, V107% ≤ 2%, V105% ≤ 10%, and Dmax ≤ 2.4 Gy. Dose limits to organs-at-risk (OAR) were summarize in supplemental materials. Twenty brain SRS and 10 breast IMRT treatment plans that were previously delivered on patients were selected and re-planned using CG. The pre and postoptimized plan parameters were compared using student t-tests.For brain SRS plans, the V100, GM, and CI for the pre- and post-Clinical-Goals plans were 93.22% ± 7.2% vs 97.96% ± 0.29% (p = 0.009), 0.63 ± 0.16 vs 0.42 ± 0.05 (p < 0.001) and 1.07 ± 0.18 vs 1.06 ± 0.06 (p = 0.79), respectively. There were no differences in max dose to OARs. In breast IMRT plans, the target V107% for pre and postimplemented plans were 16.50% ± 10.98% vs 0.32% ± 0.32%, respectively (p = 0.001). The average target V105% were 44.00% ± 15.72% and 8.69% ± 4.53%, respectively (p < 0.001). No differences were found in the average target V100% (p = 0.128) and V95% (p = 0.205). The average target Dmax were 112.28% ± 1.59% and 109.14% ± 0.73%, respectively (p < 0.001). There were only minor differences in doses to OARs.The implementation of CG in Varian Eclipse significantly improved SRS and IMRT plan quality with enhanced coverage, dose GM, and CI without increased dose to OARs.

Artificial Neuronal Devices Based on Emerging Materials: Neuronal Dynamics and Applications.

Artificial neuronal devices are critical building blocks of neuromorphic computing systems and currently the subject of intense research motivated by application needs from new computing technology and more realistic brain emulation. Researchers have proposed a range of device concepts that can mimic neuronal dynamics and functions. Although the switching physics and device structures of these artificial neurons are largely different, their behaviors can be described by several neuron models in a more unified manner. In this paper, the reports of artificial neuronal devices based on emerging volatile switching materials are reviewed from the perspective of the demonstrated neuron models, with a focus on the neuronal functions implemented in these devices and the exploitation of these functions for computational and sensing applications. Furthermore, the neuroscience inspirations and engineering methods to enrich the neuronal dynamics that remain to be implemented in artificial neuronal devices and networks toward realizing the full functionalities of biological neurons are discussed.

Survival benefit of local consolidative therapy for patients with single-organ metastatic pancreatic cancer: a propensity score-matched cross-sectional study based on 17 registries.

Background: The continuous exploration of oligometastatic disease has led to the remarkable achievements of local consolidative therapy (LCT) and favorable outcomes for this disease. Thus, this study investigated the potential benefits of LCT in patients with single-organ metastatic pancreatic ductal adenocarcinoma (PDAC). Methods: Patients with single-organ metastatic PDAC diagnosed between 2010 - 2019 were identified from the Surveillance, Epidemiology, and End Results (SEER) database. Propensity score matching (PSM) was performed to minimize selection bias. Factors affecting survival were assessed by Cox regression analysis and Kaplan-Meier estimates. Results: A total of 12900 patients were identified from the database, including 635 patients who received chemotherapy combined with LCT with a 1:1 PSM with patients who received only chemotherapy. Patients with single-organ metastatic PDAC who received chemotherapy in combination with LCT demonstrated extended median overall survival (OS) by approximately 57%, more than those who underwent chemotherapy alone (11 vs. 7 months, p < 0.001). Furthermore, the multivariate Cox regression analysis revealed that patients that received LCT, younger age (< 65 years), smaller tumor size (< 50 mm), and lung metastasis (reference: liver) were favorable prognostic factors for patients with single-organ metastatic PDAC. Conclusion: The OS of patients with single-organ metastatic pancreatic cancer who received LCT may be prolonged compared to those who received only chemotherapy. Nevertheless, additional prospective randomized clinical trials are required to support these findings.

Circuit-Level Memory Technologies and Applications based on 2D Materials.

Memory technologies and applications implemented fully or partially using emerging 2D materials have attracted increasing interest in the research community in recent years. Their unique characteristics provide new possibilities for highly integrated circuits with superior performances and low power consumption, as well as special functionalities. Here, an overview of progress in 2D-material-based memory technologies and applications on the circuit level is presented. In the material growth and fabrication aspects, the advantages and disadvantages of various methods for producing large-scale 2D memory devices are discussed. Reports on 2D-material-based integrated memory circuits, from conventional dynamic random-access memory, static random-access memory, and flash memory arrays, to emerging memristive crossbar structures, all the way to 3D monolithic stacking architecture, are systematically reviewed. Comparisons between experimental implementations and theoretical estimations for different integration architectures are given in terms of the critical parameters in 2D memory devices. Attempts to use 2D memory arrays for in-memory computing applications, mostly on logic-in-memory and neuromorphic computing, are summarized here. Finally, challenges that impede the large-scale applications of 2D-material-based memory are reviewed, and perspectives on possible approaches toward a more reliable system-level fabrication are also given, hopefully shedding some light on future research.

RapidPlan hippocampal sparing whole brain model version 2-how far can we reduce the dose?

Whole-brain radiotherapy has been the standard palliative treatment for patients with brain metastases due to its effectiveness, availability, and ease of administration. Recent clinical trials have shown that limiting radiation dose to the hippocampus is associated with decreased cognitive toxicity. In this study, we updated an existing Knowledge Based Planning model to further reduce dose to the hippocampus and improve other dosimetric plan quality characteristics. Forty-two clinical cases were contoured according to guidelines. A new dosimetric scorecard was created as an objective measure for plan quality. The new Hippocampal Sparing Whole Brain Version 2 (HSWBv2) model adopted a complex recursive training process and was validated with five additional cases. HSWBv2 treatment plans were generated on the Varian HalcyonTM and TrueBeamTM systems and compared against plans generated from the existing (HSWBv1) model released in 2016. On the HalcyonTM platform, 42 cases were re-planned. Hippocampal D100% from HSWBv2 and HSWBv1 models had an average dose of 5.75 Gy and 6.46 Gy, respectively (p < 0.001). HSWBv2 model also achieved a hippocampal Dmean of 7.49 Gy, vs 8.10 Gy in HSWBv1 model (p < 0.001). Hippocampal D0.03CC from HSWBv2 model was 9.86 Gy, in contrast to 10.57 Gy in HSWBv1 (p < 0.001). For PTV_3000, D98% and D2% from HSWBv2 model were 28.27 Gy and 31.81 Gy, respectively, compared to 28.08 Gy (p = 0.020) and 32.66 Gy from HSWBv1 (p < 0.001). Among several other dosimetric quality improvements, there was a significant reduction in PTV_3000 V105% from 35.35% (HSWBv1) to 6.44% (HSWBv2) (p < 0.001). On 5 additional validation cases, dosimetric improvements were also observed on TrueBeamTM. In comparison to published data, the HSWBv2 model achieved higher quality hippocampal avoidance whole brain radiation therapy treatment plans through further reductions in hippocampal dose while improving target coverage and dose conformity/homogeneity. HSWBv2 model is shared publicly.

Bayesian quantile nonhomogeneous hidden Markov models.

Hidden Markov models are useful in simultaneously analyzing a longitudinal observation process and its dynamic transition. Existing hidden Markov models focus on mean regression for the longitudinal response. However, the tails of the response distribution are as important as the center in many substantive studies. We propose a quantile hidden Markov model to provide a systematic method to examine the entire conditional distribution of the response given the hidden state and potential covariates. Instead of considering homogeneous hidden Markov models, which assume that the probabilities of between-state transitions are independent of subject- and time-specific characteristics, we allow the transition probabilities to depend on exogenous covariates, thereby yielding nonhomogeneous Markov chains and making the proposed model more flexible than its homogeneous counterpart. We develop a Bayesian approach coupled with efficient Markov chain Monte Carlo methods for statistical inference. Simulations are conducted to assess the empirical performance of the proposed method. The proposed methodology is applied to a cocaine use study to provide new insights into the prevention of cocaine use.

Charcot Spinal Arthropathy-Induced Progression From Upper to Lower Motor Neuron Bowel Syndrome.

Charcot spinal arthropathy is a progressively degenerative joint disorder of the vertebrae. Historically, it was a common consequence of tertiary syphilis. Currently, it is a rare complication of spinal cord injury (SCI). We present the case of a 28-year-old patient with paraplegia who developed progressive, neurogenic bowel dysfunction due to Charcot spinal arthropathy. Our patient had upper motor neuron bowel syndrome secondary to SCI which advanced to lower motor neuron bowel syndrome. Charcot spinal arthropathy should be considered as a possible cause for symptom progression in SCI patients. This case illustrates the connection between Charcot spine and lower motor neuron dysfunction in the setting of prior upper motor neuron dysfunction.

Placenta Percreta Complications.

Placenta percreta is the most severe form of placenta accreta and is characterized by placental invasion through the entirety of the myometrium and possibly into extrauterine tissues. It is associated with prior cesarean deliveries and placenta previa. Herein, we present the case of a patient who developed placenta percreta and experienced massive blood loss of 27 liters. She developed many complications over the next 11 months, including deep vein thrombosis, pulmonary embolism, preeclampsia after pregnancy, hematoma, blood clots in the bladder, lactation failure, ileus, vesicovaginal fistula, excessive scar tissue requiring surgery, loss of an ovary, and recurrent bladder perforation. We analyze the mechanisms of these complications and the most common complications associated with placenta percreta.